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Statistik Asuransi Gempa Bumi Indonesia

2016

Indonesia Earthquake Insurance Statistics 2016

PT REASURANSI MAIPARK INDONESIA

Kata PengantarForeword

Bapak dan Ibu Direksi Perusahaan Asuransi yangsaya hormati,

Dengan mengucap Puji Syukur Kehadirat TuhanYang Maha Esa, buku Laporan Statistik AsuransiGempa Bumi Indonesia (LSAGBI) Desember 2016telah selesai disusun. Penyusunan LSAGBI iniselaras dengan salah satu Visi MAIPARK yaitumenjadi Center Of Excellence dan sebagai upayadalam memberikan pelayanan yang terbaik bagiIndustri Asuransi Umum di Indonesia khususnyamengenai statistik serta pengetahuan risiko gempabumi.

Sebagai salah satu publikasi rutin, LSAGBI inimemuat hasil kajian risiko-risiko gempa bumiterkait data exposure, premi nasional, jumlah risikodan perkembangan risiko gempa bumi. Di dalamlaporan ini kami juga menyajikan ulasan aktuariadan risiko exposure dari sudut pandang asuransigempa bumi. Kami melakukan analisis atas eksposurutama kita di Jawa Bagian Barat yang kami lengkapidengan analisis Probable Maximum Loss untuk areaini. Selain itu, data statistik nasional ini jugamemperlihatkan kecenderungan penurunan produksipremi dari underwriting risiko bencana olehperusahaan asuransi di Indonesia walaupunpenurunan premi ini tidak diimbangi denganpenurunan eksposurnya.

Seperti yang kita sadari bersama, dukungan dariseluruh perusahaan asuransi umum sangat berartibagi kami dalam upaya pengembangan laporanstatistik ini agar dapat digunakan sebagai panduanyang baik dalam kita melaksanakan bisnis sehari-hari.

Akhir kata, kami menyadari kebutuhanpenyempurnaan buku ini sangat tinggi sehinggakritik maupun saran sangat kami harapkan. Semogalaporan ini dapat menjadi referensi yang berkualitasdan dapat memberikan manfaat bagi perusahaanyang menangani asuransi gempa bumisehinggadapat memperkuat industri asuransi umum diIndonesia. Kami mohon maaf apabila masihditemukan kesalahan data dan informasi yangdisajikan dalam buku ini.

Hormat kami,

Dear Sir / Madam,

Our gratitude to God Almighty, the IndonesianEarthquake Insurance Statistic Report as atDecember 2016 is ready to publish. This reportpublication is aligned with MAIPARKs vision,which is to be a "Center of Excellence and alsoto provide the best service for the GeneralInsurance Industry in Indonesia, especiallystatistic and earthquake risks knowledge.

As a regular publication, it contains earthquakerisks study related to the national Exposure,premium, number of risks and development of theearthquake risks management. We also includeactuarial and Exposure risks reviews fromearthquake insurance point of view. We do ananalysis to our main exposure in West Java thatalso provide with Probable Maximum Lossanalysis for this area. The national statistics alsoshow decreasing trend in premium productionfrom disaster risk underwriting by insurancecompanies in Indonesia although the decline inpremiums is not followed by a decrease inexposure.

Support from the General Insurance Industry willbe meaningful to us in order to improve thisStatistic Report. We realize the need forimprovement of this report is very high so thatcritics and suggestions are appreciate.

Finally, we hope that this report could be used asa qualified reference and will be beneficial for allgeneral insurance industries in Indonesia. Weapologize for any possible data and informationerrorspresented in this report.

Sincerely,

Yasril Y. Rasyid

President Director

Daftar IsiContents

Kata Pengantar...........................................................................................................................................iForeword

Daftar Isi.....................................................................................................................................................iiContents

Keadaan Ekonomi 2016- Produk Domestik Bruto 2016..........................................................................2Economic Outlook 2016- Gross Domestic Product 2016

Keadaan Asuransi 2016Insurance Outlook 2016:Premi Asuransi 2016.................................................................................................................................3Insurance Premium 2016Industri Asuransi 2016..............................................................................................................................3Insurance Industry 2016

Catatan Asuransi Gempa Bumi 2004 -2016:Earthquake Insurance 2004-2016:Premi 2004-2016 .....................................................................................................................................4Premium 2004-2016Eksposur 2004-2016 ................................................................................................................................5Exposure 2004-201610 Besar Klaim Events 2004-2016 .........................................................................................................6Top 10 Claim Events 2004-2016

Premi Asuransi Gempa Bumi 2016:Earthquake Insurance Premium 2016:Premi Per Okupasi UY 2012-2016 ......................................................................................................... 8Premium by Occupation UY 2012-2016Premi Per Interest UY 2012-2016 ......................................................................................................... 9Premium by Interest UY 2012-2016Premi: Distribusi Okupasi UY 2016 .........................................................................................................10Premium: Occupation Distribution UY 2016

Eksposur Asuransi Gempa Bumi 2016:Earthquake Insurance Exposure 2016:Eksposur Per Okupasi UY 2012-2016 ....................................................................................................11Exposure by Occupation UY 2012-2016Eksposur Per Interest UY 2012-2016 ....................................................................................................12Exposure by Interest UY 2012-2016Eksposur: Distribusi Okupasi UY 2016 ...................................................................................................13Exposure: Occupation Distribution UY 2016

Jumlah Risiko Asuransi Gempa Bumi 2016:Earthquake Insurance Number of Risk 2016:Jumlah Risiko Per Okupasi UY 2012-2016 .............................................................................................14Number of Risk by Occupation UY 2012-2016Klaim Events Asuransi Gempa Bumi 2016 ..............................................................................................15Earthquake Insurance Claim Events 2016Shakemap untuk Gempa Pidie dan Solok .................................................................................................16Shakemap for Pidie and Solok Event

Daftar Gempa Bumi 2016 Magnitudo 6.0 Mw .......................................................................................18Earthquake List 2016 Magnitude 6.0 Mw

Daftar IsiContents

Ulasan Aktuaria: Cadangan Atas Risiko Bencana ...................................................................................20Actuarial Review: Cat Reserve

Risk Analysis:Risk Analysis:Portfolio & PML Jawa Bagian Barat ........................................................................................................26Portfolio & PML of Western JavaCatatan Gempa di Jakarta .........................................................................................................................27Historical Earthquake in Jakarta

Data Detail:Detail Data:

Tabel 2.1 National Aggregate Exposure By Cresta Zone ..............................................................30Table 2.1 National Aggregate Exposure By Cresta ZoneTabel 2.2 National Aggregate Exposure By Okupasi......................................................................32Table 2.2 National Aggregate Exposure By OccupacyTabel 2.3 National Aggregate Exposure By Interest......................................................................34Table 2.3 National Aggregate Exposure By InterestTabel 2.4 National Aggregate Exposure By Province....................................................................36Table 2.4 National Aggregate Exposure By ProvinceTabel 2.5 National Aggregate Exposure By Class Construction....................................................37Table 2.5 National Aggregate Exposure By Class ConstructionTabel 3.1 National Gross Premium By Cresta Zone.......................................................................39Table 3.1 National Gross Premium By Cresta ZoneTabel 3.2 National Gross Premium By Occupation..........................................................................41Table 3.2 National Gross Premium By OccupationTabel 3.3 National Gross Premium By Interest ..............................................................................43Table 3.3 National Gross Premium By InterestTabel 3.4 National Gross Premium By Province ............................................................................45Table 3.4 National Gross Premium By ProvinceTabel 3.5 National Gross Premium By Class Construction ...........................................................46Table 3.5 National Gross Premium By Class ConstructionTabel 4.1 Number Of Risk By Cresta Zone ....................................................................................47Table 4.1 Number Of Risk By Cresta ZoneTabel 4.2 Number Of Risk By Occupation ......................................................................................50Table 4.2 Number Of Risk By OccupationTabel 4.3 Number Of Risk By Class Construction .........................................................................52Table 4.3 Number Of Risk By Class ConstructionTabel 4.4 Number Of Risk By Province ..........................................................................................54Table 4.4 Number Of Risk By ProvinceTabel 5.1 Claim Frequency By Cresta Zone ...................................................................................55Table 5.1 Claim Frequency By Cresta ZoneTabel 5.2 Claim Frequency By Occupation .....................................................................................56Table 5.2 Claim Frequency By OccupationTabel 5.3 Claim Frequency By Province .........................................................................................59Table 5.3 Claim Frequency By ProvinceTabel 5.4 Claim By Cresta Zone ......................................................................................................60Table 5.4 Claim By Cresta ZoneTabel 5.5 Claim By Occupation .......................................................................................................62Table 5.5 Claim By OccupationTabel 5.6 Claim By Province ............................................................................................................64Table 5.6 Claim By Province

Daftar IsiContents

Risk & Loss:Risk & Loss:Underwriting Year 2012 - 2016, Seluruh Okupasi .................................................................................66Underwriting Year 2012 - 2016, Seluruh OkupasiUnderwriting Year 2016, Seluruh Okupasi ..............................................................................................67Underwriting Year 2016, All OccupationUnderwriting Year 2015, Seluruh Okupasi ..............................................................................................68Underwriting Year 2015, All OccupationUnderwriting Year 2014, Seluruh Okupasi ..............................................................................................69Underwriting Year 2014, All OccupationUnderwriting Year 2013, Seluruh Okupasi ..............................................................................................70Underwriting Year 2013, All OccupationUnderwriting Year 2012, Seluruh Okupasi ..............................................................................................71Underwriting Year 2012, All OccupationUnderwriting Year 2016, Okupasi Agrikultural .......................................................................................72Underwriting Year 2016, Occupation AgriculturalUnderwriting Year 2016, Okupasi Komersial ..........................................................................................73Underwriting Year 2016, Occupation CommercialUnderwriting Year 2016, Okupasi Industrial ...........................................................................................74Underwriting Year 2016, Occupation IndustrialUnderwriting Year 2016, Okupasi Residensial ........................................................................................75Underwriting Year 2016, Occupation ResidentialUnderwriting Year 2015, Okupasi Agrikultural .......................................................................................76Underwriting Year 2015, Occupation AgriculturalUnderwriting Year 2015, Okupasi Komersial ..........................................................................................77Underwriting Year 2015, Occupation CommercialUnderwriting Year 2015, Okupasi Industrial ...........................................................................................78Underwriting Year 2015, Occupation IndustrialUnderwriting Year 2015, Okupasi Residensial ........................................................................................79Underwriting Year 2015, Occupation ResidentialUnderwriting Year 2014, Okupasi Agrikultural .......................................................................................80Underwriting Year 2014, Occupation AgriculturalUnderwriting Year 2014, Okupasi Komersial ..........................................................................................81Underwriting Year 2014, Occupation CommercialUnderwriting Year 2014, Okupasi Industrial ............................................................................................82Underwriting Year 2014, Occupation IndustrialUnderwriting Year 2014, Okupasi Residensial ........................................................................................83Underwriting Year 2014, Occupation ResidentialUnderwriting Year 2013, Okupasi Agrikultural .......................................................................................84Underwriting Year 2013, Occupation AgriculturalUnderwriting Year 2013, Okupasi Komersial ..........................................................................................85Underwriting Year 2013, Occupation CommercialUnderwriting Year 2013, Okupasi Industrial ...........................................................................................86Underwriting Year 2013, Occupation IndustrialUnderwriting Year 2013, Okupasi Residensial ........................................................................................87Underwriting Year 2013, Occupation ResidentialUnderwriting Year 2012, Okupasi Agrikultural .......................................................................................88Underwriting Year 2012, Occupation AgriculturalUnderwriting Year 2012, Okupasi Komersial ..........................................................................................89Underwriting Year 2012, Occupation CommercialUnderwriting Year 2012, Okupasi Industrial ...........................................................................................90Underwriting Year 2012, Occupation IndustrialUnderwriting Year 2012, Okupasi Residensial ........................................................................................91Underwriting Year 2012, Occupation Residential

Daftar IsiContents

Exposure, Premi Bruto, Jumlah Risiko & Klaimper Cresta Zone, Underwriting Year 2016 ..............................................................................................92Exposure, Gross Premium, Number of Risk & Claimby Cresta Zone, Underwriting Year 2016





Exposure, Premi Bruto, Jumlah Risiko & Klaimper Okupasi, Underwriting Year 2016 ......................................................................................................97Exposure, Gross Premium, Number of Risk & Claimby Occupation, Underwriting Year 2016

Exposure, Premi Bruto, Jumlah Risiko & Klaimper Okupasi, Underwriting Year 2015 98Exposure, Gross Premium, Number of Risk & Claimby Occupation, Underwriting Year 2015

Exposure, Premi Bruto, Jumlah Risiko & Klaimper Okupasi, Underwriting Year 2014 .....................................................................................................99Exposure, Gross Premium, Number of Risk & Claimby Occupation, Underwriting Year 2014

Exposure, Premi Bruto, Jumlah Risiko & Klaimper Okupasi, Underwriting Year 2013 ....................................................................................................100Exposure, Gross Premium, Number of Risk & Claimby Occupation, Underwriting Year 2013

Exposure, Premi Bruto, Jumlah Risiko & Klaimper Okupasi, Underwriting Year 2012 .............................................................................................. ......101Exposure, Gross Premium, Number of Risk & Claimby Occupation, Underwriting Year 2012

Exposure, Premi Bruto, Jumlah Risiko & Klaimper Provinsi, Underwriting Year 2016 ....................................................................................................102Exposure, Gross Premium, Number of Risk & Claimby Province, Underwriting Year 2016

Daftar IsiContents

Exposure, Premi Bruto, Jumlah Risiko & Klaimper Provinsi, Underwriting Year 2015 .....................................................................................................103Exposure, Gross Premium, Number of Risk & Claimby Province, Underwriting Year 2015



Exposure, Premi Bruto, Jumlah Risiko & Klaimper Provinsi, Underwriting Year 2012 ....................................................................................................106Exposure, Gross Premium, Number of Risk & Claimby Province, Underwriting Year 2012

Lampiran:Annexes:

Daftar Istilah Statistik ...............................................................................................................................108Statistical GlossaryTarif dan Zona Asuransi Gempa Bumi Indonesia ....................................................................................110Indonesia Earthquake Insurance Tariff and ZoneDaftar Cresta Zone Untuk Indonesia .......................................................................................................124Cresta Zone List For IndonesiaSkala untuk First Loss, Rate Polis Jangka Pendek, Periode Indemnity .................................................131Scale for Fisrt Loss, Short Period Policy Rate, Indemnity PeriodKode Okupasi ............................................................................................................................................134Occupation Code

Gambaran Ekonomi Industri Asuransi 2016Economic Insurance Industry Outlook 2016

1 |Reasuransi MAIPARK

8,564.9

8,982.5

9,433.0

Year 2014 Year 2015 Year 2016

4.88%

5.02%

IDR 9.433 Trillion

IDR 378,2 Trillion

IDR 9.433 triliun adalah total Produk Domestik Bruto (PDB) Indonesia pada tahun 2016. Tiga sektorusaha dengan kontribusi paling besar adalah: (i) Industri Pengolahan 21,39%, (ii) Perdagangan BesarEceran; Reparasi Mobil dan Sepeda Motor 13,31% dan (iii) Pertanian, Kehutanan dan Perikanan 12,82%.

IDR 9.433 Trillion is Indonesias GDP for 2016. Three sectors with highest contribution are: (i)Processing Industry 21.4%, (ii) grocery, retail and automotive trading 13.3% and (iii) Farming,Forestry and Fisheries 12.82%.

IDR 378,2 trilyun adalah besaran PDB dalam kelompok usaha Jasa Keuangan dan Asuransi. Sektor iniberkontribusi 4% dari total PDB Indonesia pada tahun 2016, masih berada di bawah sektor Konstruksi;Pertambangan dan Penggalian; Informasi dan Komunikasi.

IDR 378,2 Trillion is the GDP from Financial Services and Insurance sector. This sector contributes 4%of Indonesia's total GDP by 2016, below Construction sector; Mining and excavation; Information andCommunication.

Keadaan Ekonomi 2016. Economic Outlook 2016

Indonesia mengalami pertumbuhan ekonomi sebesar 5.02% pada tahun 2016. Namun demikian lajupertumbuhan pada sektor Jasa Keuangan dan Asuransi mencapai 8.9%, tertinggi dibandingkan sektorlainnya. Laju pertumbuhan 8.9% ini juga menjadi laju pertumbuhan tertinggi bila dibandingkan dengantahun sebelumnya yaitu 4.7% pada tahun 2014 dan 8.6% pada tahun 2015.

Indonesias economic growth in 2016 reached 5.02%. However, growth rates in the Financial Servicesand Insurance sector was 8.9%, the highest compared to other sectors. The 8.9% growth rate is alsothe highest growth rate compared to 4.7% in 2014 and 8.6% in 2015.

In trillion IDR

Reasuransi MAIPARK | 2

Source: Badan Pusat Statistik, 2016

69.01%

9.55%

21.44%

Produksi premi Asuransi Jiwa lebih tinggi dibandingkan dengan Asuransi Umum/kerugian. Dari Totalproduksi premi 2016 sebesar 199 Triliun rupiah, Asuransi Jiwa berkontribusi sebesar 69,01%.Sedangkan Asuransi Umum/Kerugian hanya 30,99%. Dari total Asuransi Umum tersebut ada 30%merupakan produksi premi Asuransi Umum/Kerugian dari Lini Bisnis Harta Benda.

Life Insurance premium production is higher than General Insurance/Non Life. Life Insurancecontributed 69.01% of 199 Trillion rupiah, the total premium production in 2016. While GeneralInsurance / Non Life is only 30.99%. The premium production of General Insurance / Non Life from theBusiness Line of Property is 30% of the total Premium of General Insurance.

6PerusahaanReasuransi

Reinsurance

77Perusahaan

Asuransi Umum

General Insurance

55Perusahaan

Asuransi Jiwa

Life Insurance

5Asuransi

Wajib dan Sosial

Mandatory and Social Insurance

Keadaan Asuransi 2016. Insurance Outlook 2016

AJAU-O

AU-P

Uraian/Description Premi /Premium

Asuransi Jiwa/Life Insurance (AJ)

137,785,583

Asuransi Umum - Harta Benda/General Insurance - Property (AU-P)

19,072,900

Asuransui Umum - Lainnya/ General Insurance - Others (AU-O)

42,797,300

In million IDR


Source: Otoritas Jasa Keuangan, 2017

Asosiasi Asuransi Umum Indonesia, 2017

Source: Otoritas Jasa Keuangan, 2017

Grafik ini menampilkan pertumbuhan premi dari underwiring year (UY) 2004-2016. UY 2016 masihbelum matang per 31 Januari 2017, dan diperkirakan masih bertambah sampai dengan akhir tahun2017 nanti. Bila melihat dari pola UY sebelumnya, maka penambahan premi untuk UY 2016 sendiridapat mencapai kira-kira 19% atau sekitar Rp4,3 Triliun.Dengan proyeksi UY 2016 menjadi Rp4,3 Triliun, maka dapat kita lihat terjadi penurunan produksipremi dari UY 2015 ke UY 2016 untuk asuransi gempa bumi sebesar 19%. Hal ini berbandingterbalik dengan pertumbuhan Jasa Keuangan dan Asuransi secara umum di Indonesia yang tumbuhsebesar 8,9%.

This graph shows premium growth from underwriting year 2004-2016. UY 2016 is still developingas obtained per January 31, 2017, and still increasing until the end of 2017. Looking at the previousUY pattern, the additional premium for UY 2016 can reach approximately 19% or about Rp4.3Trillion.The projection of UY 2016 to 4.3 Trillion, we can see a decrease in premium production forearthquake insurance from UY 2015 to UY 2016 by 19%. This is inversely related to the growth ofFinancial Services and Insurance in general in Indonesia which grew by 8.9%.

Catatan Asuransi Gempa Bumi 2004 - 2016. Earthquake Insurance 2004 2016.

3.1

3.2

3.3

Premi di UY 2016 terdistribusi 56,7% di Cresta 3yang mencakup 3 Area yaitu 3.1- Provinsi DKIJakarta, 3.2-Kota Bandung dan 3.3-Kota/KabupatenLain selain Jakarta dan Bandung di Provinsi Bantendan Jawa Barat.

Premiums in UY 2016 were distributed 56.7% inCresta 3 that covering 3 Area ie 3.1- Province ofDKI Jakarta, 3.2-City of Bandung and 3.3-City/Regency Other than Jakarta and Bandung in Bantenand West Java Province.

Cresta 356,7%

871

1,334

1,645

2,076 1,982 1,782

2,116

2,823 2,721

4,530

4,959

5,305

3,659

-

1,000

2,000

3,000

4,000

5,000

6,000

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

In B

illions I

DR



Grafik ini menampilkan pertumbuhan eksposur dari underwiring year 2004-2016. Untuk UY 2016dimana angka eksposur didapatkan per 31 Januari 2017 kami perkirakan masih dapat bertambahsampai dengan akhir tahun 2017 nanti. Bila melihat dari pola UY sebelumnya, maka penambahaneksposur untuk UY 2016 sendiri dapat mencapai kira-kira 20% atau sekitar Rp3,4 Kuadriliun.Dengan proyeksi UY 2016 menjadi 3,4 Quadtriliun maka dapat kita lihat terjadi penurunan eksposurdari UY 2015 ke UY 2016 untuk asuransi gempa bumi sebesar 5,6%.

This graph shows exposure growth from underwiring year 2004-2016. Exposure of UY 2016 isobtained as of January 31, 2017, we estimate it can still increase until the end of 2017. Whenlooking at the previous UY pattern, the additional exposure for UY 2016 can reach approximately20% or about Rp3.4 QuadTrillion.The projection of UY 2016 to Rp3.4 QuadTrillion, we can see a decrease in exposure production forearthquake insurance from UY 2015 to UY 2016 by 5.6%.

Cresta 353,2%

3.1

3.2

3.3

Eksposur di UY 2016 terdistribusi 53,2% di Cresta3 yang mencakup 3 Area yaitu 3.1- Provinsi DKIJakarta, 3.2-Kota Bandung dan 3.3-Kota/KabupatenLain selain Jakarta dab Bandung di Provinsi Bantendan Jawa Barat.

Exposure in UY 2016 are distributed 53.2% inCresta 3 that covering 3 Area ie 3.1- Province ofDKI Jakarta, 3.2-City of Bandung and 3.3-City/Regency Other than Jakarta and Bandung in Bantenand West Java Province.

692

1,026

1,458 1,606

1,481 1,498

1,671

2,156 1,974

2,759

3,115

3,642

2,884

-

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

In T

rillio

ns I

DR


Dilihat dari sisi intensitas gempa yang digambarkan oleh intensitas maksimum, Gempa Aceh 2004,Gempa Yogyakarta 2006 dan Gempa Padang 2009 adalah tiga gempa terbesar yang terjadi daritahun 2004. Loss ratio tertinggi terjadi pada event Gempa Aceh 2004 dan Gempa Padang 2009masing-masing sebesar 77,85% dan 78,38% sementara itu Gempa Yogyakarta 2006 hanya memilikiloss ratio 1.25%.

Fakta ini salah satunya disebabkan mekanisme gempa dimana pada gempa subduksi (Aceh danPadang) intensitas tinggi dapat terjadi pada area yang sangat luas sehingga loss ratio sangat tinggi,sedangkan gempa patahan (Yogyakarta) intensitas tinggi hanya berdampak pada area yang relativelebih kecil, sehingga loss ratio tidak terlalu besar.

From the earthquake intensity point of view, three biggest earthquake are Aceh EQ 2004, Yogya EQ2006 and Padang EQ 2009. the highest loss ratio experienced in Aceh and Padang events each77,85% dan 78,38% while Yogya EQ only resulted a minor loss ratio of 1.25%.

One of the probable cause is the mechanism of earthquake where the subduction type of EQ (Acehand Padang) affected larger high intensity areas than the fault EQ (Yogya), which this was affectedthe loss ratio of these particular events.


No.Kejadian Tanggal

Kejadian

Kekuatan

Gempa

Kedalaman

(KM)

MMI

Maksimum

Eksposure

Terdampak

Klaim Rasio Kerugian

Number Event Date of Loss Magnitude Depth (KM) Maximum MMI Affected Exposure Claim Loss Ratio

1 ACEH 26/12/04 9.1 Mw 30.00 IX 958,757.93 746,364.47 77.85%

2 YOGYA 27/05/06 6.3 Mw 12.50 IX 22,607,377.57 283,523.14 1.25%

3 PADANG 06/03/07 6.3 Mwc 11.00 VIII 5,975,640.89 28,753.80 0.48%

4 BENGKULU 12/09/07 8.4 Mw 34.00 VIII 870,834.57 61,180.16 7.03%

5 PADANG 16/08/09 6.7 Mwc 20.00 VI 3,105,777.10 42,782.94 1.38%

6 TASIKMALAYA 02/09/09 7.0 Mw 46.00 VII 37,059,693.77 33,508.29 0.09%

7 PADANG 30/09/09 7.6 Mw 81.00 IX 1,434,892.35 1,124,652.86 78.38%

8 BIMA 09/11/09 6.6 Mwc 18.00 VI 10,261,909.77 47,771.65 0.47%

9 MERAPI 25/10/10 6,912,282.28 30,534.72 0.44%

10 KELUD 13/02/14 51,122,470.86 270,749.97 0.53%

volcanic eruption

volcanic eruption


In million IDR

Asuransi Gempa Bumi 2012 - 2016Earthquake Insurance 2012-2016


Tabel di atas adalah data produksi premi selama 5 tahun terakhir. Pada Okupasi Agrikultur, kenaikanpaling tinggi terjadi antara UY 2011-2012 yaitu sebesar 23,1%. Pada Okupasi Komersial danResidential, kenaikan paling tinggi terjadi antara UY 2012-2013 yaitu masing-masing sebesar175,3% dan 36,6%. Pada Okupasi Industrial kenaikan paling tinggi terjadi antara UY 2013-2014yaitu sebesar 44,7%.Bila angka UY 2016 dibandingkan dengan UY 2012 maka kenaikan produksi premi tertinggi terjadipada Okupasi Komersial yaitu sebesar 52,2%. Sementara itu, premi dari okupasi agrikulturmengalami penurunan sebesar 7.8%.

The table above is the premium production for the last 5 years. In the Agricultural Occupation, thehighest increase occurred between UY 2011-2012 that is equal to 23,1%. In the Commercial andResidential Occupation, the highest increase occurred between UY 2012-2013, i.e 175.3% and36.6%, respectively. In Industrial Occupation, the highest increase occurred between UY 2013-2014which is equal to 44,7%.If the figure of UY 2016 compared to UY 2012, the highest increase of premium production occurredin Commercial Occupation that is equal to 52,2%. Meanwhile, the premium amount from agriculturaloccupation decreased 7.8%.

Premi Asuransi Gempa Bumi 2016Earthquake Insurance Premium 2016

UY Agrikultural Komersial Industrial Residensial Total

2012 29.42 23.1% 658.34 -13.2% 1,686.07 -0.8% 347.54 1.5% 2,721.36 -3.6%

2013 35.25 19.8% 1,812.23 175.3% 2,208.13 31.0% 474.72 36.6% 4,530.33 66.5%

2014 28.85 -18.2% 1,119.71 -38.2% 3,195.59 44.7% 614.99 29.5% 4,959.13 9.5%

2015 29.55 2.5% 1,802.33 61.0% 2,897.32 -9.3% 575.52 -6.4% 5,304.72 7.0%

2016 27.34 -7.5% 1,001.97 -44.4% 2,155.41 -25.6% 474.48 -17.6% 3,659.21 -31.0%

2012 to 2016 -7.1% 52.2% 27.8% 36.5% 34.5%


In billion IDR

Tabel di atas adalah data produksi premi selama 5 tahun terakhir. Pada Interest Building, Others danBusiness Interuption, kenaikan paling tinggi terjadi antara UY 2012-2013 yaitu masing-masingsebesar 47,1%, 74,5% dan 274,7%. Pada Interest Machinery, kenaikan paling tinggi terjadi antaraUY 2013-2014 yaitu sebesar 35,4%. Pada Interest Stock, kenaikan paling tinggi terjadi antara UY2013-2014 yaitu sebesar 73,8%.

Bila angka UY 2016 dibandingkan dengan UY 2012 maka pertumbuhan tertinggi produksi premiterjadi pada Interest Building yaitu sebesar 41,1%. Sementara pertumbbuhan terendah terjadi padainterest Stock sebesar 17.4%.

The table above is the premium production data for the last 5 years. In the Building, Others andBusiness Interuption Interest, the highest increase occurred between UY 2012-2013, i.e 47.1%,74.5% and 274.7% respectively. In the Machinery Interest, the highest increase occurred betweenUY 2013-2014 which is 35.4%. In the Stock Interest, the highest increase occurred between UY2013-2014 which is equal to 73.8%.

If the figure of UY 2016 compared to UY 2012, the highest growth in premium production occurs inthe Interest Building that is equal to 41.1%, which the lowest growth is come from Stocks 17.4%.


UY Building Machinery Others Stock Bussiness

Interruption Total

2012 1,109.99 -14.3% 540.32 -3.6% 452.83 39.4% 377.17 2.7% 241.06 -12.5% 2,721.36 -3.6%

2013 1,632.36 47.1% 694.83 28.6% 790.30 74.5% 509.68 35.1% 903.15 274.7% 4,530.33 66.5%

2014 2,036.80 24.8% 940.76 35.4% 619.12 -21.7% 885.58 73.8% 476.87 -47.2% 4,959.13 9.5%

2015 2,754.09 35.2% 920.39 -2.2% 637.03 2.9% 576.81 -34.9% 416.38 -12.7% 5,304.72 7.0%

2016 1,565.76 -43.1% 708.82 -23.0% 620.01 -2.7% 442.68 -23.3% 321.93 -22.7% 3,659.21 -31.0%

2012 to 2016 41.1% 31.2% 36.9% 17.4% 33.5% 34.5%


In billion IDR

Kode Ukupasi dan KeteranganDescription & Occupation Code

Jumlah RisikoNumber of Risk

PremiPremium

ExposureExposure

Private Building 297 76.292 507.543.348.366,93 356.534.521.091.664,00

Trading and storage 293 60.265 454.438.237.985,54 352.215.048.368.483,00

Hotels, Entertainment, Sport,

Services294 8.779 258.685.294.963,58 189.853.581.229.423,00

Conventional power station . . 281 242 254.200.632.649,54 184.559.210.302.184,00

Mass communication 292 9.639 184.847.249.329,61 122.983.990.617.974,00

Edible fats, edible oil and desiccated

coconut producers274 638 135.087.154.106,32 116.242.728.581.394,00

Tobacco, cigars and cigarettes

manufacture279 404 134.531.401.654,95 133.199.023.516.826,00

Mining (underground or above

ground) of precious metal . . .200 26 126.611.096.193,12 88.838.188.575.950,00

Cement, Chalk, Lime and Gypsum

Industry211 213 122.352.807.348,26 105.505.506.691.319,00

Industrial, Mining and Commercial

Machinery . . .221 963 107.635.376.746,15 100.860.184.394.393,00



In IDR

Tabel di atas adalah data eksposur selama 5 tahun terakhir. Pada Okupasi Agrikultur, kenaikanpaling tinggi terjadi antara UY 2011-2012 yaitu sebesar 16,7%. Pada Okupasi Komersial danResidential, kenaikan paling tinggi terjadi antara UY 2012-2013 yaitu masing-masing sebesar117,5% dan 37,5%. Pada Okupasi Industrial, kenaikan paling tinggi terjadi antara UY 2013-2014yaitu sebesar 38,4%.

Bila angka UY 2016 dibandingkan dengan UY 2012 maka kenaikan eksposur tertinggi terjadi padaOkupasi Komersial yaitu sebesar 60,1%.

The table above is the exposure for the last 5 years. In the Agricultural Occupation, the highestincrease occurred between UY 2011-2012 that is equal to 16,7%. In the Commercial and ResidentialOccupation, the highest increase occurred between UY 2012-2013, i.e 117.5% and 37.5%respectively. In the Industrial Occupation, the highest increase occurred between UY 2013-2014 i.e38,4%.

If the number of UY 2016 compared to UY 2012, the highest increase in exposure occurred atCommercial Occupation that is equal to 60,1%.

Eksposur Asuransi Gempa Bumi 2016Earthquake Insurance Exposure2016


In Trillion IDR

UY Agri Commercial Industrial Residensial Total

2012 23.54 -99.9% 458.52 -99.9% 1,261.13 -99.9% 231.08 -99.9% 1,974.28 -99.9%

2013 25.96 10.3% 997.42 117.5% 1,417.71 12.4% 317.64 37.5% 2,758.72 39.7%

2014 22.35 -13.9% 746.06 -25.2% 1,961.85 38.4% 385.09 21.2% 3,115.35 12.9%

2015 24.20 8.3% 1,163.69 56.0% 2,068.54 5.4% 385.82 0.2% 3,642.25 16.9%

2016 21.73 -10.2% 734.06 -36.9% 1,797.00 -13.1% 331.69 -14.0% 2,884.48 -20.8%

2012 to 2016 -7.7% 60.1% 42.5% 43.5% 46.1%

Tabel di atas adalah data eksposure selama 5 tahun terakhir. Pada Interest Others dan BusinessInteruption kenaikan paling tinggi terjadi antara UY 2012-2013 yaitu sebesar 53,2% dan 143,1%.Pada Interest Machinery dan Stock kenaikan paling tinggi terjadi antara UY 2013-2014 yaitusebesar 31,9% dan 58,7%. Pada Interest Building kenaikan paling tinggi terjadi antara UY 2014-2015 yaitu sebesar 41,1%.

Bila angka UY 2016 dibandingkan dengan UY 2012 maka kenaikan eksposure terjadi pada InterestBuilding yaitu sebesar 57,2%.

The table above is the exposure for the last 5 years. In The Others and Business InteruptionInterest, the highest increase occurred between UY 2012-2013, i.e 53.2% and 143.1%. In TheMachinery and Stock Interest, the highest increase occurred between UY 2013-2014 i.e 31.9% and58.7%. In the Building Interest, the highest increase occurred between UY 2014-2015 i.e 41.1%.

If the number of UY 2016 compared to UY 2012, the exposure increase occurs in the BuildingInterest i.e 57.2%.

Eksposur Asuransi Gempa Bumi 2016Earthquake Insurance Exposure2016

UY Building Machinery Others Stock Bussiness

Interruption Total

2012 804.2 -16.6% 395.8 -8.3% 309.6 22.2% 281.1 -1.1% 183.6 -17.2% 1,974.3 -8.4%

2013 1,048.3 30.4% 452.1 14.2% 474.3 53.2% 337.5 20.1% 446.4 143.1% 2,758.7 39.7%

2014 1,290. 3 23.1% 596.2 31.9% 401.3-

15.4%535.8 58.7% 291.7 -34.6% 3,115.3 12.9%

2015 1,820.3 41.1% 655.4 9.9% 449.8 12.1% 424.9 -20.7% 291.8 0.0% 3,642.3 16.9%

2016 1,195.4 -34.3% 573.0 -12.6% 486.5 8.2% 365.0 -14.1% 264.5 -9.4% 2,884.5 -20.8%

2012 to 2016 48.6% 44.8% 57.2% 29.9% 44.0% 46.1%

Reasuransi MAIPARK |12

In Trillion IDR

Eksposur Asuransi Gempa Bumi 2016Earthquake Insurance Exposure 2016


Kode Okupasi dan KeteranganDescription & Occupation Code

Jumlah RisikoNumber of Risk

PremiPremium

EksposureExposure

Private Building 297 76,292 507,543,348,367 356,534,521,091,664

Trading and storage 293 60,265 454,438,237,986 352,215,048,368,483

Hotels, Entertainment, Sport,

Services294 8,779 258,685,294,964 189,853,581,229,423

Conventional power station,

buildings with boiler houses . . .281 242 254,200,632,650 184,559,210,302,184

Tobacco, cigars and cigarettes

manufacture279 404 134,531,401,655 133,199,023,516,826

Mass communication 292 9,639 184,847,249,330 122,983,990,617,974

Edible fats, edible oil and

desiccated coconut producers274 638 135,087,154,106 116,242,728,581,394

Cement, Chalk, Lime and Gypsum

Industry211 213 122,352,807,348 105,505,506,691,319

Industrial, Mining and Commercial

Machinery, . . .221 963 107,635,376,746 100,860,184,394,393

Petrochemical works 232 133 99,884,265,243 89,767,796,081,455

In IDR

Tabel di atas adalah data Jumlah Risiko selama 5 tahun terakhir. Pada Okupasi Agrikultur,penurunan paling rendah terjadi antara UY 2014-2015 yaitu sebesar 47,1%. Pada Okupasi Komersialpenurunan paling rendah terjadi antara UY 2015-2016 yaitu sebesar 32,9%. Pada Okupasi Industrialkenaikan paling tinggi terjadi antara UY 2011-2012 yaitu sebesar 14,7%. Pada Okupasi Residensialkenaikan paling tinggi terjadi antara UY 2013-2014 yaitu sebesar 30,3%.

Bila angka UY 2016 dibandingkan dengan UY 2012 maka kenaikan Jumlah Risiko tertinggi terjadipada Okupasi Residensial yaitu sebesar 24,0%.

The table above is the Number of Risk for the last 5 years. In The Agricultural Occupation, thelowest decrease occurred between UY 2014-2015 i.e 47,1%. In The Comercial Occupation, thelowest decrease occurred between UY 2015-2016 i.e 32,9%. In the Industrial Occupation, thehighest increase occurred between UY 2011-2012 i.e 14,7%. In Residential Occupational, thehighest increase occurred between UY 2013-2014 i.e 30.3%.

If the number of UY 2016 compared to UY 2012, the highest increase of Number of Risk occurs inResidential Occupation i.e 24.0%.

Jumlah Risiko Asuransi Gempa Bumi 2016Earthquake Insurance Number of Risk 2016

UY Agrikultural Komersial Industrial Residensial Total

2012 420 10.8% 94,598 3.5% 17,199 14.7% 61,627 4.2% 173,844 4.8%

2013 535 27.4% 100,976 6.7% 18,461 7.3% 70,315 14.1% 190,287 9.5%

2014 753 40.7% 124,569 23.4% 18,906 2.4% 91,629 30.3% 235,857 23.9%

2015 398 -47.1% 129,298 3.8% 17,604 -6.9% 86,333 -5.8% 233,633 -0.9%

2016 503 26.4% 86,784 -32.9% 18,744 6.5% 76,443 -11.5% 182,474 -21.9%

2012 to 2016 19.8% -8.3% 9.0% 24.0% 5.0%


Tabel di atas menampilkan kejadian Gempabumi yang terjadi selama 2016. Data MMI, Kedalamandan Magnitude merujuk kepada data USGS. Meskipun Gempa Air Bangis berkekuatan relativebesar yaitu 7,8Mw namun hanya dirasakan pada intensitas III sehingga mengakibatkan kerugianrelative kecil. Hal ini berbeda dengan Gempa Solok dan Pidie Jaya, dengan kekuatan 6,6 Mw dan6,5 Mw dirasakan hingga MMI VII dan IX mengakibatkan kerugian yang besar.

Pada table tersebut disajikan informasi Exposure Terdampak yang diperoleh dari data exposureon-risk atas risiko yang terdampak pada saat kejadian gempa. Sedangkan Loss Ratio disinidimaksudkan sebagai perbandingan antara Jumlah Klaim terhadap Exposure Terdampak. LossRatio terbesar adalah event Solok sebesar 3,68%.

The table above shows the earthquake events that occurred during 2016. MMI, Depth andMagnitude refer to USGS data. Although the Air Bangis earthquake is relatively large i.e 7.8Mwand felt to MMI III, it has resulted in relatively small losses. This is different from the Solok andPidie Jaya Earthquakes, which has magnitude of 6.6 Mw and 6.5 Mw is felt until MMI VII and IXresulted in substantial losses.

The table is presented information Affected Exposure from data exposure onrisk for the risks thatwere affected at the time of the earthquake. Loss Ratio here is intended as a comparison betweenthe Claim Amounts to Affected Exposure. Loss Ratio for event Solok is the largest i.e 3.68%.

Claim Events Asuransi Gempa Bumi 2016Earthquake Insurance Claim Events 2016

Event Date of Loss Magnitude Depth (km)Max Intensity

(MMI)Affected Exposure Claim Loss Ratio

Halmahera 24/02/16 5.4 Mb 13.70 I 18,548.39 - 0.000000%

Air Bangis 02/03/16 7.8 Mw 24.00 III 2,521,420.33 10.00 0.000397%

Mentawai 05/03/16 5.4 Mw 10.00 I 5,051,567.91 10.00 0.000198%

Garut 06/04/16 6.1 Mw 29.00 IV 166,952.86 59.91 0.035884%

Bengkulu 10/04/16 5.7 Mw 41.00 V 1,071,880.14 - 0.000000%

Solok 02/06/16 6.6 Mw 50.00 VII 573,578.45 21,102.04 3.679015%

Bengkulu 12/09/16 8.4 Mw 34.00 VIII 1,775,903.72 - 0.000000%

Padang 21/09/16 4.1 Mw 162.50 I 19,320,111.46 25.00 0.000129%

Irian Jaya 17/10/16 4.7 Mw 33.40 I 2,526,522.97 - 0.000000%

Manado 27/10/16 5.8 Mw 61.00 IV 2,752,815.31 5.55 0.000202%

Malang 16/11/16 5.7 Mw 85.00 IV 6,009,281.11 - 0.000000%

Pidie Jaya 07/12/16 6.5 Mw 8.20 IX 771,388.24 2,350.00 0.304646%

In million IDR


Gambar 5. Peta Intensitas dan ShakeMap Gempa Solok. Gambar 6. Peta Intensitas dan ShakeMap Gempa Pidie Jaya.

Peta di atas menggambarkan penyebaran intensitas MMI merujuk kepada data USGS.Berdasarkan peta tersebut dapat diperoleh informasi wilayah-wilayah yang memiliki MMI I-X+. Gempa Solok yang terjadi pada tanggal 2 Juni 2016 menimbulkan MMI maksimal VII yangberdampak di wilayah Kota Sungai Penuh. Sedangkan Gempa Pidie Jaya yang terjadi pada 7Desember 2016 menimbulkan MMI hingga IX yang berdampak pada wilayah Kabupaten PidieJaya.

The map above illustrates the spread of MMI intensity referring to USGS data. Based on themap it can be obtained information areas that have MMI I-X+. Solok earthquake that occurredon June 2, 2016 raises the maximum MMI VII that impacts in the area of City of Sungai Penuh.While the Pidie Jaya Earthquake that occurred on December 7, 2016 cause MMI to IX whichaffects the Regency of Pidie Jaya.

Claim Events Asuransi Gempa Bumi 2016Earthquake Insurance Claim Events 2016


Source: US. Geological Survey, 2017

Katalog Gempa Bumi 2016Earthquake Catalog 2016


Daftar Gempa Bumi 2016 Magnitudo 6.0 MwEarthquake List 2016 Magnitude 6.0 Mw

No. Tanggal Kejadian Bujur Lintang USGS BMKG Kota Terdekat

Number Date of Loss Longitude Latitude Magnitude (Mw) Depth (KM) Magnitude

(Mw)

Depth (KM) Nearest Population

1 1/11/2016 126.97 BT 3.8 LU 6.50 13 6.4 1058 KM Tenggara

KEP-TALAUD-SULUT

2 2/12/2016 119.34 BT 9.77 LS 6.30 28 6.6 1014 KM Barat Daya

SUMBABARAT-NTT


KEP-MENTAWAI-SUMBAR


GARUT-JABAR


TANGGAMUS-LAMPUNG

6 6/2/2016 100.46 BT 2.29 LS 6.60 50 6.5 7279 KM Barat Daya PESISIR

SELATAN-SUMBAR

7 6/5/2016 125.74 BT 4.63 LS 6.30 429.6 6.3 445146 KM Barat Daya

BURUSELATAN-MALUKU


SUMBAWA BARAT-NTB

9 8/24/2016 122.54 BT 7.46 LS 6.0 533 6.1 537105 KM Barat Laut FLORES

TIMUR-NTT

10 10/9/2016 127.41 BT 1.79 LU 5.8 128 6.2 11752 KM Barat Laut

HALMAHERA BARAT-MALUT

11 10/19/2016 108 BT 5.29 LS 6.6 614 6.5 654120 KM Timur Laut

SUBANG-JABAR

12 10/27/2016 125.79 BT 1.32 LU 5.8 61 6.1 1075 KM Tenggara

BITUNG-SULUT


LEBAK-BANTEN

14 11/16/2016 113.12 BT 9.32 LS 5.7 85 6.2 69127 KM Tenggara

KAB MALANG-JATIM

15 12/5/2016 123.4 BT 7.32 LS 6.3 526 6.4 524120 KM Timur Laut

FLORES TIMUR-NTT

16 12/7/2016 96.36 BT 5.19 LU 6.5 13 6.4 1018 KM Timur Laut

KAB PIDIE JAYA-ACEH

17 12/21/2016 128.01 BT 7.75 LS 6.7 152 6.6 173184 KM Timur Laut

MALUKU BARAT DAYA

18 12/30/2016 118.63 BT 9.37 LS 6.3 79 6.6 9159 KM Barat Laut SUMBA

BARAT DAYA-NTT


Ulasan Aktuaria: Cadangan Atas Risiko Bencana

Actuarial Review: Cat Reserve


Ulasan Aktuaria Actuarial Review

CADANGAN ATAS RISIKO BENCANA CATASTROPHIC RESERVE

Penerapan Catastrophic Reserve di Indonesia Otoritas Jasa Keuangan (OJK) baru-baru ini menerbitkan Rancangan Surat Edaran OJK (RSEOJK) Nomor /SEOJK.05/2016 tentang pedoman pembentukan cadangan teknis bagi perusahaan asuransi dan perusahaan reasuransi. RSEOJK tersebut akan menggantikan Peraturan Ketua Badan Pengawas Pasar Modal dan Lembaga Keuangan Nomor PER-09/BL/2012 yang mulai berlaku untuk laporan keuangan perusahaan periode 31 Desember 2017. Termuat beberapa perubahan dan penambahan atas pedoman yang berlaku sebelumnya, salah satu tambahan yang akan dibahas pada ulasan aktuaria periode ini yaitu mengenai pembentukan cadangan atas risiko bencana (catastrophic reserve). Bahasan meliputi perbandingan penerapan di beberapa negara dan jabaran metodologi perhitungan terkait. Dalam poin terakhir RSEOJK tersebut menjelaskan bahwa cadangan atas risiko katastrop dihitung berdasarkan manfaat asuransi retensi sendiri setelah dikurangi cadangan premi dengan memperhitungkan kemungkinan terjadinya risiko katastrop, dimana definisi risiko bencana atau risiko katastrop yaitu risiko kerugian yang timbul akibat terjadinya fenomena alam atau risiko murni kecelakaan yang menyebabkan kerugian besar bagi perusahaan. Definisi Probable Maximum Loss Penerapan catastrophic reserve di berbagai belahan dunia beragam, sesuai dengan kondisi industri perasuransian dan tingkat perlindungan yang ingin dicapai. Beberapa negara menjadikan Probable Maximum Loss (PML) sebagai dasar dalam pembentukan catastrophic reserve. PML adalah suatu ukuran risiko terkait kerugian terbesar perusahaan yang memungkinkan diperkirakan terjadi, seringkali definisi PML juga sebagai periode ulang (return period), yang merupakan kebalikan dari probabilitas bahwa nilai kerugian akan melebihi suatu nilai tertentu. PML umumnya diperoleh dari simulasi pemodelan bencana yang berisi kemungkinan kejadian bencana beserta perkiraan nilai kerugiannya dengan menggunakan data kejadian (catalogue sintesis) dan karakteristik peril secara stokastik. Penentuan return period bergantung terhadap jenis kejadian bencana alam (gempa bumi, banjir, longsor dll) yang memiliki karakteristik berbeda-beda.

Implementation of Cat Reserve in Indonesia Otoritas Jasa Keuangan (OJK) recently issued Rancangan Surat Edaran OJK (RSEOJK) Number /SEOJK.05/2016 about guideline for the establishment of technical reserve to insurance and reinsurance companies. It will replace Peraturan Ketua Badan Pengawas Pasar Modal dan Lembaga Keuangan Number PER-09/BL/2012, begins at financial report in 31 December 2017. The contents are modifications and additions of prior guideline, one of the additions that will be discussed in this actuarial commentary is the establishment of catastrophic reserve. The topic involves comparison of practice in other countries and methodology calculation. The last point in RSEOJK explains calculation of catastrophic reserve based on the advantage in own retention insurance after subtracted from premium reserve and consider the occurrence probability of catastrophic risk, which the definition of catastrophic risk is damage risk from consequence of natural phenomena or pure accident risk that cause losses significantly for company. Definition of Probable Maximum Loss The practice of catastrophic reserve vary widely in different part of the world, which suitable with insurance industry and level of protection to be achieved. Most countries makes Probable Maximum Loss (PML) as the basis of its establishment. PML is a measure of risk corresponding to the largest loss the company can reasonably be expected to experience. Often, PML is defined as a return period, which is the inverse of the probability that losses will exceed a dollar threshold. Commonly PML is obtained by simulating the Catastrophe Model, which contains the occurrence possibility of catastrophe event and its estimated loss respectively using synthetic catalogue and the characteristic of perils stochastically. Determination of return period depend on type of catastrophe events (i.e. earthquake, flood, landslide, etc.) that has different characteristics.


Penerapan Catastrophic Reserve di Negara Lain Perusahaan asuransi umum di Jepang membentuk catastrophic reserve hingga mencapai nilai estimasi kerugian yang disebabkan oleh satu kejadian bencana alam yang terjadi sekali dalam 70 tahun (Topan Vera 1959). Regulasi asuransi bencana alam di Meksiko menetapkan pembentukan catastrophic reserve dengan limit sebesar 90% dari PML untuk return period kejadian gempa bumi sebesar 150 tahun dan probabilitas terjadi kerugian melebihi suatu nilai kerugian terbesar tertentu yaitu sebesar 10%, termasuk persentil 90% atas kurva kerentanan. OSFI (Office of the Superintendent of Financial Institutions) di Kanada mengatur tentang ERC (Earthquake Reserve Complement) sebagai penambah dari cadangan premi gempa bumi sebagai bentuk persiapan finansial industri perasuransian. ERC dibentuk berdasarkan pengurangan antara PML250 dan beberapa komponen yaitu proteksi reasuransi, retensi, pembiayaan dari pasar modal dan cadangan premi gempa bumi. Perusahaan memiliki kurun waktu selama 25 tahun untuk membangun PML menuju level PML500 yang harus dicapai pada akhir tahun fiskal 2022. Agar lebih memahami penjelasan diatas, berikut disajikan formulasi beserta keterangan sebagai berikut:

The Practice of Catastrophic Reserve in Other Countries General insurance company in Japan must establish catastrophic reserve until the amount reaches the estimated loss caused by a natural disaster which occurs once in 70 years (i.e. typhoon Vera in 1959). Regulation of catastrophic insurance in Mexico set the establishment of catastrophic reserve until the limit in the amount of 90% of PML for return period of the event of 150 years and there is a 10% of probability for the maximum value of damage that it would exceed involving a percentile of 90% over the vulnerability curve. OSFI (Office of the Superintendent of Financial Institutions) in Canada regulate ERC (Earthquake Reserve Complement) as addition of earthquake premium reserve as financial preparedness for insurance industry. ERC is formed by subtraction among PML250 and some components that is reinsurance protections, retention, capital market financing and earthquake premium reserve. Companies have 25 years to build their PML to the PML500 level that must be reached by the end of fiscal year 2022. In order to achieve better understanding, the formulation and the description of each components are

Earthquake Reserve Formula by OSFI

ERRO = EPR + ERC

ERC = PML250 + N/25 (PML500 PML250) Reinsurance Collectable Retention

Approved Capital Market Financing EPR, where :

ERRO earthquake reserve required by OSFI

EPR earthquake premium reserve, which consist of the voluntary

accumulation of the earthquake premiums as defined below. This

EPR must be less than or equal to net PML500. Any earthquake

premium contributed to the EPR must remain in the EPR unless

there is a material decrease in exposure.

Earthquake Premiums an amount not exceeding 75 per cent of (current years earned

policyholders earthquake premiums cost of earthquake

reinsurance).

In the case of catastrophic reinsurance coverage not specifically

written for earthquake risks, an allocation of the premium

amount must be made. Companies should be able to demonstrate

the reasonableness of their rate-making procedures.

ERC earthquake reserve complement, the additional component (if

necessary) of ERRO needed to achieve financial preparedness

according to the formula. The ERC must always be greater than

or equal to 0.

N current fiscal year minus 1997

Gross PML PML amount estimated after policyholders deductibles but

before reinsurance protection, based on the higher value

between Quebec and British Columbia total losses on personal

and commercial property caused by shake and fire.

Net PML PML amount estimated after policyholders deductibles and after

reinsurance protection.

PML250 gross PML estimated using a 250 year event return period at a

75 per cent damageability confidence level for deterministic

models or a 250 year loss return period at a 50 per cent

damageability confidence level for probabilistic models.

PML500 gross PML estimated using a 500 year event return period at a

75 per cent damageability confidence level for deterministic

models or a 500 year loss return period at a 50 per cent

damageability confidence level for probabilistic models.

Retention amount of retention the company is currently using to manage

its earthquake exposure subject to a maximum of 10 per cent of

Capital & Surplus


Pemanfaatan Catastrophe Model dalam pembentukan Catastrophic Reserve Salah satu hasil keluaran Catastrophe Model yang berguna bagi manajemen risiko adalah kurva Occurrence Exceedance Probability (OEP). Kurva OEP merupakan representatif grafis dari probabilitas bahwa pada suatu level kerugian akan terlewati dalam periode waktu yang ditentukan. Secara spesifik, kurva tersebut berharga bagi perusahaan asuransi dan reasuransi untuk menentukan besaran dan distribusi dari kerugian potensial portofolio, serta memperoleh nilai PML. PML berkaitan dengan return period yang secara sederhana merupakan kebalikan (inverse) dari probabilitas exceedance tahunan. Sebagai contoh, pada gambar 2.5 dibawah menunjukkan nilai PML untuk return period kerugian 250 tahun sebesar kurang lebih 21 Juta Dolar, yang merupakan nilai batas bawah kerugian pada probabilitas exceedance sebesar 0,4%. Secara jelas, hal ini berarti ada probabilitas 99,6% bahwa suatu perusahaan perasuransian menderita kerugian sampai dengan kurang lebih 21 Juta Dolar dan ada 0,4% kemungkinan kerugian lebih dari 21 Juta Dolar.

The Application of Catastrophe Model to Establish Catastrophe Reserve One of the Catastrophe Model output that has advantage for risk management is Occurrence Exceedance Probability (OEP) curve. An OEP curve is a graphical representation of the probability that a certain level of loss will be surpassed in a given time period. Specifically, an OEP curve is particularly valuable for insurers and reinsurers to determine the size and distribution of their portfolios potential losses, and also to obtain PML. PML limits are framed in terms of a return period that simply is inverse of the annual probability of exceedance. In this example, from the figure 2.5 below, it can be seen that the PML is approximately 21 million Dollar on return period of event 250 years, as the lower limit on the loss at a 0.4% probability of exceedance. Clearly, there is a 99.6% probability for the company have loss until approximately 21 million Dollar and there is a 0.4% probability of loss more than 21 million Dollar.

(Source: Grossi, P., Kunreuther, H. 2005. Catastrophe Modelling: A New Approach to Managing Risk. Springer Science + Business Media, Inc.)


Pada praktiknya, terdapat banyak kemungkinan modifikasi perhitungan OEP. Salah satu metodologi yang akan dibahas yaitu berdasarkan Grossi (2005). Setiap kejadian bencana diasumsikan hanya dapat terjadi maksimum sebanyak 1 (satu) kali, sehingga frekuensi masing-masing kejadian bencana , yaitu berdistribusi Bernoulli dengan fungsi massa peluang berupa

There are many probable modification of calculation OEP in practice. One of the calculation methodology that will be discussed in this actuarial commentary is based on Grossi 2005. Each of catastrophic event assumed only occur maximum once, so that the frequency of each catastrophic event , that is has Bernoulli distribution with probability mass function

= ,

(1 ), .

Isi tabel ELT (Earthquake Loss Table) yang diperoleh dari Catastrophe Model kemudian diurutkan berdasarkan mean loss dari yang terbesar hingga terkecil, sehingga menjadi

The content of earthquake loss table from catastrophe model then sorted by mean loss from the largest to the smallest, so that

1 > 2 > >

merupakan statistik terurut (order statistic). Berdasarkan statistika terurut, dapat

didefinisikan dengan menggunakan dua persamaan berikut:

is order statistic. Based on order statistic,

is defined as two following equation:

: = 1 , (1)

: = 1 , > (2)

sesuai dengan persamaan (1) maka,

corresponding with the equation (1),

= = 1 1

1

=1

.

apabila terdapat nilai mean loss yang sama, dimana untuk suatu nilai = +1, maka

If there are mean loss that have equal value, which is for a , the value of = +1, then

+1 = .


Secara implisit, penggunaan PML sebagai batas minimum pendanaan bersifat adil bagi perusahaan karena telah mempertimbangkan sebaran dan jenis eksposur sesuai dengan portofolio masing-masing, hal ini tercermin karena merupakan data masukan untuk Catastrophe Model, sehingga catastrophic reserve yang dihasilkan mencerminkan nilai risiko yang ditanggung. Suatu perusahaan yang memiliki eksposur berukuran besar akan mencadangkan sejumlah dana lebih besar pula terhadap perusahaan lain yang memiliki eksposur berukuran kecil atau eksposur sama besar namun banyak tersebar pada daerah dengan tingkat risiko bencana yang rendah. Akhirnya, penetapan standar kekuatan finansial dan aturan skema perlindungan terhadap perusahaan asuransi dan reasuransi yang memiliki liability atas risiko katastrop akan menjadikan industri asuransi lebih kuat dan lebih terencana sehingga diharapkan memiliki masa depan yang baik untuk terus berkontribusi dalam perekonomian nasional maupun internasional.

Implicitly, the use of PML as a minimum funding limit is fairly for the company because it has considered spreading and type of exposure in accordance with its respective portfolio, this is because it is the input data for catastrophe model, so the resulting reflects the value of the assure risks. A company that has large exposure will reserve a larger amount of funds against other companies that have small exposures or exposures as large but widely spread in areas with low level of disaster risk. Finally, the establishment of financial strength standards and protection schemes for insurance and reinsurance companies that have a liability for catastrophic risk will make the insurance industry stronger and more planned so it is expected to have a good future to continue to contribute in the national and international economy.

Referensi/Refferrence: 1. Grossi, P., Kunreuther, H. 2005. Catastrophe Modelling: A New Approach to Managing

Risk. Springer Science + Business Media, Inc. 2. Grossi, P., Kunreuther, H., Editors. 2013. Clarification and Errata to Catastrophe

Modeling: A New Approach to Managing Risk. 3. 2017. Rancangan Surat Edaran Otoritas Jasa Keuangan perihal Pedoman Pembentukan

Cadangan Teknis bagi Perusahaan Asuransi dan Perusahaan Reasuransi. Otoritas Jasa Keuangan.

4. PT Reasuransi Maipark. 2010. Cadangan Katastrofi Gempa Bumi. 5. Hapsari, I.N., Atmaja, F.W. 2016. Terminologi dan Perhitungan Dasar Analisa Risiko

Katastrofe. RDI PT Reasuransi Maipark. 6. 2013. Guideline B-9 Earthquake Exposure Sound Practices. Office of the

Superintendent of Financial Institutions Canada. 7. Rodrguez, N.A.R. 2016. Regulation of Catastrophic Insurance : Mexico. Insurance and

Surety National (CNSF-MEXICO). 8. 2007. Fact Book 2005 2006 General Insurance in Japan

(www.sonpo.or.jp/en/publication/pdf/fb2006e.pdf). The International Department The General Insurance Association of Japan.


http://www.sonpo.or.jp/en/publication/pdf/fb2006e.pdfhttp://www.sonpo.or.jp/en/publication/pdf/fb2006e.pdfhttp://www.sonpo.or.jp/en/publication/pdf/fb2006e.pdfhttp://www.sonpo.or.jp/en/publication/pdf/fb2006e.pdfhttp://www.sonpo.or.jp/en/publication/pdf/fb2006e.pdfhttp://www.sonpo.or.jp/en/publication/pdf/fb2006e.pdfhttp://www.sonpo.or.jp/en/publication/pdf/fb2006e.pdfhttp://www.sonpo.or.jp/en/publication/pdf/fb2006e.pdfhttp://www.sonpo.or.jp/en/publication/pdf/fb2006e.pdfhttp://www.sonpo.or.jp/en/publication/pdf/fb2006e.pdfhttp://www.sonpo.or.jp/en/publication/pdf/fb2006e.pdf

Risk Analysis: Western Java


West Java is the largest contributor of exposure and number ofrisk i.e 1.5 trillion rupiah or 53.2% of total exposure and 79,787number of risks. The PML simulation is performed with inputfrom exposure, occupation, interest and building height data ofthe region.

Jawa Bagian Barat adalahpenyumbang eksposur danjumlah risiko terbesar yaitumasing-masing 1.5 triliun rupiahatau 53.2% dari total eksposuredan 79.787 jumlah risiko.Simulasi PML dilakukanmempertimbangkan data okupasi,ekposure, interest dan tinggibangunan.

IDR 1.534 T

53.2%

79,787 Ratio to National Exposure

Number of Risks

Total Exposure

B

43.87%

C

47.15%

BI

8.98%

Eksposur per InterestB: BuildingC: Content

BI: Business Interuption

HR

27.53%

MR

4.77%

LR

67.70%

Eksposur per Tinggi Bangunan

HR: High Rise BuildingMR: Medium Rise

BuildingLW: Low Rise Building

0.012%0.273%

0.531%

1.272%

1.935%2.047%

3.46%

0.000%

0.500%

1.000%

1.500%

2.000%

2.500%

3.000%

3.500%

0 500 1000 1500 2000

(1) Disimulasikan di iAsuransi

(2) Hanya mensimulasikan guncangan gempa untuk interest building dan content saja


Historical Earthquake in Jakarta.Pieces of the book "Die Erdbeben des Indischen Archipels bis Zum Jahre 1857" written by Arthur Wichmann published in 1918.

Wichmann noted the earthquake that occurred on January 22, 1780 is felt in Jakarta. From other old references, we estimate the earthquake intensity was at least VII MMI with the epicenter estimated at the subduction zone in southern part of java, OR, local fault in Jakarta.

This earthquake is probably the biggest earthquake ever felt in Jakarta. Wichmann noted that the aftershock from the earthquake was still felt in Jakarta at least until 13 December 1780, almost a year after the incident.

Catatan Gempa di Jakarta.Potongan dari buku Die Erdbeben des Indischen Archipels bis Zum Jahre 1857 yang ditulis oleh Arthur Wichmann yang diterbitkan pada tahun 1918.

Wichmann mencatat gempa yang terjadi pada 22 Januari 1780 yang dirasakan di Jakarta. Dari deskripsi referensi lainnya, kami memperkirakan intensitas gempa ini paling tidak VII MMI dengan episenter gempa diperkirakan di zona subduksi lempeng di Selatan Jawa atau patahan lokal di Jakarta sendiri.

Gempa ini mungkin adalah gempa terbesar yang pernah dirasakan di Jakarta. Wichmann mencatat aftershock dari gempa ini masih dirasakan di Jakarta paling tidak sampai dengan 13 Desember 1780, hampir satu tahun setelah kejadian.

Sources: Die Erdbeben des Indischen Archipels bis Zum Jahre 1857, 1918

Research, Development and Innovation Group, Maipark, 2017


Observatorium Mohr.

Mulai dibangun oleh Johan Mohr pada tahun 1765 dan selesai pada tahun 1768. Bangunanini, mungkin, adalah bangunan pencakar langit pertama yang berdiri di Jakarta dengantingginya sekitar 30 meter. Ilustrasi bagnunan ini dapat dilihat pada lukisan Johannes Rach yang menggambarkan Klenteng Jin De Yuan (Vihara Dharma Bhakti sekarang) dengan latar belakang Observatorium Mohr (sumber: Arsip Perpustakaan Nasional).

Bangunan mengalami kerusakan berat secara struktur akibat gempa yang terjadi pada 22 Januari 1780 sehingga tidak lagi digunakan sebagai observatorium. Saat ini sisa-sisa bangunan tidak dapat dilihat lagi, namun dipercaya bahwa lokasi observatorium ini ada di area Glodok, di sekitar Jalan Kemurnian, tepatnya di Gang Torong.

Torong sendiri kemungkin berasal dari salah-lafal atas kata Belanda Toren yang dalam Bahasa Indonesia berarti Menara.

Mohr Observatory

It was built by Johan Mohr in 1765 and completed in 1768. The building, most likely, is the first "skyscraper" building in Jakarta with the height of about 30 meters. Illustration of the building was painted by Johannes Rach depicting Jin De Yuan Temple (Dharma Bhakti Temple now) with background of Mohr Observatory (source: National Library archive).

The building structures was severely damaged due to the earthquake that occurred on January 22, 1780 so it is no longer used as an observatory. Currently the remains of the building can not be seen again, but it is believed that the location of this observatory is in the area of Glodok, around Jalan Kemurnian, in Gang Torong.

Torong himself may be derived from "mis-pronunciation" for the Dutch word "Toren" which in Indonesian means "Tower".

Sources: National Library of Indonesia, 2017

A Bare Outpost of Learned European Culture on the Edge of the Jungles of Java, Zuidervaart and van Gent, 2004


Data Detail Detail Data


PT REASURANSI MAIPARK INDONESIA Desember 2016

30

Detail Data

Reasuransi MAIPARK |

Table 2.1 Dalam Rupiah

Amount % Amount % Amount % Amount % Amount %

BANDA ACEH 1,1 3.887.973.784.587,20 0,20 4.631.184.023.255,03 0,17 6.475.470.127.856,06 0,21 6.078.889.378.887,71 0,17 1.290.809.652.665,53 0,04

MEDAN 1,2 39.930.432.753.351,10 2,02 48.022.219.701.013,20 1,74 52.108.491.977.901,30 1,67 63.971.010.160.823,10 1,76 39.660.652.063.716,60 1,37

OTHERS 1,3 39.955.090.750.405,30 2,02 49.698.880.730.421,50 1,80 58.546.862.994.854,90 1,88 72.980.439.422.052,30 2,00 31.543.259.510.407,10 1,09

NORTH SUMATERA 1 83.773.497.288.343,60 4,24 102.352.284.454.690,00 3,71 117.130.825.100.612,00 3,76 143.030.338.961.763,00 3,93 72.494.721.226.789,30 2,51

PADANG 2,1 19.057.509.310.139,10 0,97 27.255.920.995.628,50 0,99 30.371.668.475.169,30 0,97 36.691.953.915.733,70 1,01 21.712.902.342.711,70 0,75

PALEMBANG 2,2 19.002.600.284.678,90 0,96 23.229.423.263.542,00 0,84 21.681.739.572.366,00 0,70 33.804.577.962.742,60 0,93 10.929.500.713.238,80 0,38

OTHERS 2,3 134.584.557.967.246,00 6,82 150.972.998.453.966,00 5,47 201.126.177.692.770,00 6,46 291.560.184.622.143,00 8,00 199.997.080.244.813,00 6,93

SOUTH SUMATERA 2 172.644.667.562.064,00 8,74 201.458.342.713.137,00 7,30 253.179.585.740.305,00 8,13 362.056.716.500.619,00 9,94 232.639.483.300.763,00 8,07

JAKARTA 3,1 400.178.211.429.859,00 20,27 435.509.747.123.867,00 15,79 657.274.895.593.947,00 21,10 614.671.253.265.775,00 16,88 525.292.946.002.536,00 18,21

BANDUNG 3,2 11.047.671.688.987,80 0,56 10.569.155.344.981,00 0,38 11.931.177.385.427,90 0,38 16.376.276.621.582,70 0,45 38.319.064.281.669,90 1,33

OTHERS 3,3 662.600.932.721.902,00 33,56 987.924.686.310.558,00 35,81 1.125.271.475.245.590,00 36,12 1.009.114.935.603.370,00 27,71 970.845.665.192.685,00 33,66

WEST JAVA 3 1.073.826.815.840.750,00 54,39 1.434.003.588.779.410,00 51,98 1.794.477.548.224.970,00 57,60 1.640.162.465.490.720,00 45,03 1.534.457.675.476.890,00 53,20

SEMARANG 4,1 7.152.358.175.509,66 0,36 8.897.919.788.005,73 0,32 8.633.225.679.742,90 0,28 14.628.262.030.225,10 0,40 18.310.096.657.351,80 0,63

YOGYAKARTA 4,2 9.305.354.884.895,54 0,47 8.575.091.703.780,61 0,31 14.173.252.946.765,00 0,45 395.704.206.157.153,00 10,86 16.089.822.775.850,90 0,56

OTHERS 4,3 102.786.967.600.585,00 5,21 136.822.997.958.859,00 4,96 147.501.933.285.596,00 4,73 197.467.815.456.252,00 5,42 172.042.095.703.775,00 5,96

CENTRAL JAVA 4 119.244.680.660.990,00 6,04 154.296.009.450.645,00 5,59 170.308.411.912.104,00 5,47 607.800.283.643.630,00 16,69 206.442.015.136.978,00 7,16

SURABAYA 5,1 51.264.598.090.249,70 2,60 134.263.168.475.156,00 4,87 76.721.427.230.315,60 2,46 74.468.101.155.800,20 2,04 59.868.961.289.120,10 2,08

OTHERS 5,2 242.018.455.804.202,00 12,26 233.213.828.062.384,00 8,45 339.837.153.558.289,00 10,91 373.409.575.818.694,00 10,25 267.144.671.544.593,00 9,26

EAST JAVA 5 293.283.053.894.452,00 14,86 367.476.996.537.540,00 13,32 416.558.580.788.604,00 13,37 447.877.676.974.494,00 12,30 327.013.632.833.713,00 11,34

KALIMANTAN 6 101.470.729.177.791,00 5,14 128.384.115.815.410,00 4,65 143.455.664.012.414,00 4,60 161.577.754.961.137,00 4,44 138.424.761.726.260,00 4,80

UJUNG PANDANG 7,1 10.444.854.149.882,30 0,53 14.184.711.558.838,50 0,51 24.286.053.554.519,00 0,78 19.845.266.682.189,10 0,54 17.315.654.766.166,60 0,60

OTHERS 7,2 54.000.807.954.031,10 2,74 39.313.808.513.031,70 1,43 99.196.918.662.308,80 3,18 134.802.452.331.010,00 3,70 125.724.261.519.719,00 4,36

SULAWESI 7 64.445.662.103.913,30 3,26 53.498.520.071.870,20 1,94 123.482.972.216.828,00 3,96 154.647.719.013.199,00 4,25 143.039.916.285.885,00 4,96

OTHER ISLANDS 8 65.594.970.726.182,70 3,32 317.250.758.401.997,00 11,50 96.752.011.275.839,20 3,11 125.101.957.964.710,00 3,43 229.968.143.407.862,00 7,97

1.974.284.077.254.480,00 100,00 2.758.720.616.224.700,00 100,00 3.115.345.599.271.680,00 100,00 3.642.254.913.510.280,00 100,00 2.884.480.349.395.140,00 100,00

U/Y 2016

As At 31/12/2016

National Aggregate Exposure By Cresta Zone

T O T A L

U/Y 2015Cresta Zone

U/Y 2014U/Y 2012 U/Y 2013


31

Detail Data



32

Detail Data



AGRICULTURAL A 23.543.592.772.030,10 1,19 25.959.872.979.597,60 0,94 22.353.349.178.317,20 0,72 24.201.941.385.615,10 0,66 21.730.920.669.521,80 0,75

COMMERCIAL C 458.524.023.223.053,00 23,22 997.415.945.057.721,00 36,16 746.057.454.316.297,00 23,95 1.163.694.911.736.550,00 31,95 734.056.255.303.797,00 25,45

INDUSTRIAL I 1.261.131.480.936.090,00 63,88 1.417.705.248.445.410,00 51,39 1.961.847.545.169.260,00 62,97 2.068.542.661.256.370,00 56,79 1.796.998.918.265.550,00 62,30

RESIDENTIAL R 231.084.980.323.316,00 11,70 317.639.549.741.966,00 11,51 385.087.250.607.799,00 12,36 385.815.399.131.740,00 10,59 331.694.255.156.277,00 11,50

1.974.284.077.254.480,00 100,00 2.758.720.616.224.700,00 100,00 3.115.345.599.271.680,00 100,00 3.642.254.913.510.280,00 100,00 2.884.480.349.395.140,00 100,00

U/Y 2012 U/Y 2013

Table 2.2

Occupation

Dalam Rupiah

As At 31/12/2016

National Aggregate Exposure By Occupation

T O T A L

U/Y 2014 U/Y 2015 U/Y 2016


33

Detail Data



34

Detail Data


Table 2.3


Building 804,229,599,899,853.00 40.74 1,048,349,473,052,820.00 38.00 1,290,296,525,005,220.00 41.42 1,820,298,353,037,060.00 49.98 1,195,399,704,797,040.00 41.44

Machinery 395,825,191,670,355.00 20.05 452,117,181,591,479.00 16.39 596,214,444,173,001.00 19.14 655,388,811,517,528.00 17.99 573,046,809,741,510.00 19.87

Others 309,557,841,717,582.00 15.68 474,344,808,757,433.00 17.19 401,340,028,232,438.00 12.88 449,829,469,571,238.00 12.35 486,506,516,776,063.00 16.87

Stock 281,045,067,821,749.00 14.24 337,535,210,092,717.00 12.24 535,786,640,886,770.00 17.20 424,923,281,005,429.00 11.67 365,022,972,796,062.00 12.65

Bussiness Interruption 183,626,376,144,946.00 9.30 446,373,942,730,248.00 16.18 291,707,960,974,244.00 9.36 291,814,998,379,017.00 8.01 264,504,345,284,470.00 9.17

T O T A L 1,974,284,077,254,490.00 100.00 2,758,720,616,224,700.00 100.00 3,115,345,599,271,680.00 100.00 3,642,254,913,510,280.00 100.00 2,884,480,349,395,140.00 100.00

As At 31/12/2016

U/Y 2012 U/Y 2013

National Aggregate Exposure By Interest

In IDR

InterestU/Y 2016U/Y 2014 U/Y 2015


35

Detail Data



36

Detail Data


Table 2.4

Dalam Rupiah


01. NAD IDN_AC 8.330.818.304.607,12 0,42 9.116.947.993.870,24 0,33 10.818.584.806.308,20 0,35 14.379.423.506.970,30 0,39 5.380.260.357.777,99 0,19

02. SUMATERA UTARA IDN_SU 75.442.678.983.736,50 3,82 93.235.336.460.819,50 3,38 106.312.240.294.304,00 3,41 128.650.915.454.793,00 3,53 67.114.460.869.011,30 2,33

03. SUMATERA BARAT IDN_SB 27.135.262.374.470,70 1,37 37.470.086.268.379,00 1,36 41.845.358.771.184,50 1,34 49.659.005.247.927,30 1,36 28.253.972.639.836,40 0,98

04. RIAU IDN_RI 33.317.463.443.743,90 1,69 48.615.931.869.134,50 1,76 58.996.103.414.354,90 1,89 129.680.699.128.360,00 3,56 59.850.610.377.085,00 2,07

05. JAMBI IDN_JA 11.004.236.229.073,80 0,56 11.027.439.296.302,30 0,40 12.050.695.936.456,10 0,39 13.828.404.521.245,10 0,38 7.750.704.286.782,16 0,27

06. SUMATERA SELATAN IDN_SS 40.908.911.211.970,40 2,07 40.741.181.098.229,90 1,48 50.211.781.173.756,00 1,61 68.951.953.268.130,90 1,89 60.399.654.893.516,40 2,09

07. BENGKULU IDN_BE 4.459.798.610.968,84 0,23 4.927.169.916.451,64 0,18 5.729.351.521.639,80 0,18 4.863.623.919.342,92 0,13 4.086.018.044.082,77 0,14

08. LAMPUNG IDN_LA 28.324.556.957.773,60 1,43 26.873.983.280.511,00 0,97 39.256.265.543.780,90 1,26 44.151.362.365.851,30 1,21 30.689.862.165.182,50 1,06

09. KEP BANGKA-BELITUNG IDN_BB 3.776.611.255.319,56 0,19 4.513.635.596.734,47 0,16 6.103.165.169.682,55 0,20 6.805.282.893.318,28 0,19 2.717.411.893.232,14 0,09

10. KEPULAUAN RIAU IDN_KR 23.717.827.478.743,10 1,20 27.288.915.387.394,10 0,99 38.986.864.209.450,10 1,25 44.116.385.156.443,90 1,21 38.891.249.001.046,00 1,35

11. JAKARTA IDN_JK 400.178.211.429.859,00 20,27 435.509.747.123.867,00 15,79 657.274.895.593.947,00 21,10 614.671.253.265.775,00 16,88 525.292.946.002.536,00 18,21

12. JAWA BARAT IDN_JB 456.403.106.331.360,00 23,12 717.053.038.819.795,00 25,99 783.112.482.715.067,00 25,14 700.722.183.216.893,00 19,24 650.219.428.081.023,00 22,54

13. JAWA TENGAH IDN_JT 107.599.465.093.588,00 5,45 142.776.481.304.505,00 5,18 148.541.529.730.657,00 4,77 208.111.246.524.456,00 5,71 182.321.787.357.152,00 6,32

14. DIY IDN_YO 11.645.215.567.402,70 0,59 11.519.528.146.140,70 0,42 21.766.882.181.447,70 0,70 399.689.037.119.175,00 10,97 24.120.227.779.825,40 0,84

15. JAWA TIMUR IDN_JI 293.283.053.894.452,00 14,86 367.476.996.537.540,00 13,32 416.558.580.788.604,00 13,37 447.877.676.974.494,00 12,30 327.013.632.833.713,00 11,34

16. BANTEN IDN_BT 217.245.498.079.530,00 11,00 281.440.802.835.745,00 10,20 354.090.169.915.954,00 11,37 324.769.029.008.055,00 8,92 358.945.301.393.332,00 12,44

17. BALI IDN_BA 50.312.484.051.839,30 2,55 301.862.890.252.825,00 10,94 73.928.771.653.928,20 2,37 98.194.363.765.407,50 2,70 110.266.744.863.076,00 3,82

18. NUSA TENGGARA BARAT IDN_NB 3.872.366.415.500,33 0,20 4.543.691.637.473,73 0,16 8.220.909.960.838,64 0,26 8.856.612.465.773,41 0,24 96.734.050.307.391,80 3,35

19. NUSA TENGGARA TIMUR IDN_NT 2.139.957.825.720,15 0,11 1.792.048.433.774,98 0,06 2.560.938.828.914,01 0,08 3.167.832.755.336,99 0,09 2.798.793.371.781,08 0,10

20. KALIMANTAN BARAT IDN_KB 6.322.259.558.617,00 0,32 8.017.171.974.455,02 0,29 13.060.719.630.834,00 0,42 12.900.356.973.462,70 0,35 18.540.687.173.413,20 0,64

21. KALIMANTAN TENGAH IDN_KT 11.800.058.892.551,60 0,60 17.180.226.505.757,70 0,62 24.063.887.319.199,00 0,77 27.088.119.414.164,20 0,74 20.332.163.810.375,40 0,70

22. KALIMANTAN SELATAN IDN_KS 14.661.265.575.615,70 0,74 37.224.639.834.420,90 1,35 32.842.177.655.598,80 1,05 39.039.677.124.134,90 1,07 24.756.596.948.528,60 0,86

23. KALIMANTAN TIMUR IDN_KI 68.687.145.151.006,50 3,48 65.962.077.500.776,60 2,39 73.494.008.406.782,10 2,36 81.599.623.159.628,10 2,24 71.613.568.379.205,70 2,48

24. SULAWESI UTARA IDN_SA 11.251.956.214.549,50 0,57 16.552.762.958.990,10 0,60 15.958.081.630.250,60 0,51 18.195.501.341.054,60 0,50 20.445.722.875.841,60 0,71

25. SULAWESI TENGAH IDN_ST 2.137.386.276.386,25 0,11 4.306.545.509.307,53 0,16 3.849.472.835.005,92 0,12 8.294.869.064.730,03 0,23 6.288.630.784.363,72 0,22

26. SULAWESI SELATAN IDN_SN 26.808.326.792.795,70 1,36 27.680.222.474.512,10 1,00 93.622.862.383.802,30 3,01 105.724.916.915.420,00 2,90 107.661.108.277.026,00 3,73

27. SULAWESI TENGGARA IDN_SG 9.055.150.591.377,57 0,46 1.533.300.623.593,63 0,06 4.276.898.599.298,50 0,14 16.555.170.975.378,90 0,45 2.370.288.596.010,50 0,08

28. GORONTALO IDN_GO 14.073.410.601.299,70 0,71 2.138.788.462.312,58 0,08 2.742.222.995.389,40 0,09 2.702.625.861.650,60 0,07 1.555.177.189.289,52 0,05

29. SULAWESI BARAT IDN_SR 1.119.431.627.504,72 0,06 1.286.900.043.154,22 0,05 3.033.433.773.081,09 0,10 3.174.634.854.965,01 0,09 4.718.988.563.353,74 0,16

30. MALUKU IDN_MA 1.327.598.149.671,43 0,07 1.914.536.567.995,64 0,07 2.560.734.786.547,66 0,08 2.850.015.428.812,55 0,08 2.214.842.344.463,39 0,08

31. MALUKU UTARA IDN_MU 805.164.815.352,22 0,04 576.528.585.138,84 0,02 969.187.967.347,49 0,03 1.512.637.310.778,74 0,04 3.402.854.567.045,71 0,12

32. PAPUA BARAT IDN_PB 3.465.126.538.532,94 0,18 2.626.139.443.092,17 0,10 2.880.666.234.319,36 0,09 3.827.934.489.583,61 0,11 8.026.273.394.618,31 0,28

33. PAPUA IDN_PA 3.672.272.929.566,26 0,19 3.934.923.481.696,27 0,14 5.630.801.843.943,88 0,18 6.692.561.749.016,80 0,18 6.524.584.559.486,08 0,23

34. KALIMANTAN UTARA IDN_KU 0,00 0,00 0,00 0,00 -5.129.000.000,00 0,00 949.978.289.747,37 0,03 3.181.745.414.737,01 0,11

T O T A L 1.974.284.077.254.480,00 100,00 2.758.720.616.224.700,00 100,00 3.115.345.599.271.670,00 100,00 3.642.254.913.510.280,00 100,00 2.884.480.349.395.140,00 100,00

National Aggregate Exposure By Province

As At 31/12/2016

Cresta IDU/Y 2012 U/Y 2013 U/Y 2014 U/Y 2016U/Y 2015

Province


37

Detail Data


Table 2.5


Com: Steel, Wood, RC 9 storeys 1.739.497.719.966.750,00 88,11 2.542.061.387.940.000,00 92,15 2.795.522.026.684.000,00 89,73 3.230.067.955.033.770,00 88,68 2.499.649.841.147.960,00 86,66

Com: Steel, Wood, RC > 9 storeys 194.625.023.123.136,00 9,86 176.216.578.520.602,00 6,39 263.430.511.003.054,00 8,46 358.659.094.906.116,00 9,85 329.233.240.879.571,00 11,41

Com: Others 5.129.394.840.992,94 0,26 4.721.644.278.177,34 0,17 1.450.044.485.416,91 0,05 782.227.581.472,95 0,02 203.189.072.736,00 0,01

DW: Steel, Wood, RC up to 3

storeys

34.960.895.682.529,10 1,77 35.681.725.356.528,50 1,29 54.828.813.455.151,20 1,76 52.707.171.608.011,20 1,45 55.371.051.294.878,40 1,92

DW: Others 71.043.641.075,00 0,00 39.280.129.388,00 0,00 114.203.644.055,00 0,00 38.464.380.910,00 0,00 23.027.000.000,00 0,00

T O T A L 1.974.284.077.254.480,00 100,00 2.758.720.616.224.700,00 100,00 3.115.345.599.271.680,00 100,00 3.642.254.913.510.280,00 100,00 2.884.480.349.395.140,00 100,00

National Aggregate Exposure By Class Construction

As At 31/12/2016

Dalam Rupiah

Class ConstructionU/Y 2012 U/Y 2013 U/Y 2014 U/Y 2015 U/Y 2016

DEFINISI Konstruksi Commercial Objek pertanggungan dengan kode okupasi selain rumah tinggal (Kode Okupasi selain 2976) Com: Steel, Wood, RC 9 Konstruksi bangunan menggunakan rangka Baja, Kayu, Beton Bertulang, dengan jumlah lantai sampai dengan 9 lantai Com: Steel, Wood, RC > 9 Konstruksi bangunan menggunakan rangka Baja, Kayu, Beton Bertulang, dengan jumlah lantai lebih dari 9 lantai Com: Others Konstruksi bangunan tanpa menggunakan rangka Baja, Kayu, dan Beton Bertulang Dwelling House Objek pertanggungan rumah tinggal dengan kode okupasi 2976 (Semua kelas konstruksi) DW: Steel, Wood, RC Konstruksi rumah tinggal yang menggunakan rangka Baja, Kayu, Beton Bertulang DW: Others Konstruksi rumah tinggal tanpa menggunakan rangka Baja, Kayu,dan Beton Bertulang


38

Detail Data



39

Detail Data


Tabel 3.1 dalam Rupiah

U/Y 2012 U/Y 2013

Jumlah % Jumlah % Jumlah % Jumlah % Jumlah %

BANDA ACEH 1,1 7.358.293.470,29 0,27 8.793.701.049,39 0,19 13.175.990.968,21 0,27 11.625.725.840,44 0,22 2.459.724.588,54 0,07

MEDAN 1,2 49.827.911.349,22 1,83 65.225.644.089,02 1,44 72.231.421.117,80 1,46 97.395.138.970,28 1,84 56.665.971.585,58 1,55

OTHERS 1,3 52.403.721.273,16 1,93 76.017.524.243,55 1,68 94.612.511.486,36 1,91 125.543.446.458,11 2,37 51.152.378.053,43 1,40

NORTH SUMATERA 1 109.589.926.092,67 4,03 150.036.869.381,96 3,31 180.019.923.572,37 3,63 234.564.311.268,83 4,42 110.278.074.227,55 3,01

PADANG 2,1 35.949.744.998,21 1,32 52.543.456.587,23 1,16 58.717.717.986,33 1,18 70.898.894.811,36 1,34 41.083.748.436,32 1,12

PALEMBANG 2,2 17.787.082.345,65 0,65 24.542.961.246,33 0,54 23.372.692.200,10 0,47 36.373.138.187,42 0,69 11.004.742.635,53 0,30

OTHERS 2,3 156.346.919.869,20 5,75 195.194.119.942,24 4,31 264.887.769.256,94 5,34 408.274.946.744,67 7,70 248.446.198.241,45 6,79

SOUTH SUMATERA 2 210.083.747.213,06 7,72 272.280.537.775,80 6,01 346.978.179.443,37 7,00 515.546.979.743,45 9,72 300.534.689.313,30 8,21

JAKARTA 3,1 600.169.541.308,52 22,05 701.997.854.131,14 15,50 1.070.190.763.705,70 21,58 969.893.201.477,70 18,28 771.131.239.632,76 21,07

BANDUNG 3,2 16.558.379.845,44 0,61 16.527.423.857,51 0,36 19.311.451.635,96 0,39 28.947.891.191,18 0,55 73.284.147.924,71 2,00

OTHERS 3,3 967.779.960.990,05 35,56 1.694.251.046.769,74 37,40 1.998.139.788.770,56 40,29 1.510.203.805.588,67 28,47 1.228.702.121.351,29 33,58

WEST JAVA 3 1.584.507.882.144,01 58,22 2.412.776.324.758,39 53,26 3.087.642.004.112,22 62,26 2.509.044.898.257,55 47,30 2.073.117.508.908,76 56,65

SEMARANG 4,1 8.914.059.462,70 0,33 11.847.921.064,86 0,26 12.211.358.128,11 0,25 17.086.022.131,21 0,32 18.279.960.986,56 0,50

YOGYAKARTA 4,2 14.164.989.798,35 0,52 13.361.783.722,89 0,29 21.793.636.267,64 0,44 675.064.394.872,19 12,73 23.277.296.232,44 0,64

OTHERS 4,3 128.804.340.529,71 4,73 186.347.935.913,42 4,11 210.807.073.148,74 4,25 261.979.535.912,33 4,94 224.678.113.269,38 6,14

CENTRAL JAVA 4 151.883.389.790,76 5,58 211.557.640.701,17 4,67 244.812.067.544,49 4,94 954.129.952.915,73 17,99 266.235.370.488,38 7,28

SURABAYA 5,1 65.663.614.886,45 2,41 220.186.001.263,86 4,86 106.206.639.468,21 2,14 89.195.876.108,31 1,68 61.934.785.178,59 1,69

OTHERS 5,2 314.184.442.768,54 11,55 342.507.818.893,02 7,56 497.428.406.279,90 10,03 442.687.545.005,39 8,35 266.346.736.841,22 7,28

EAST JAVA 5 379.848.057.654,99 13,96 562.693.820.156,88 12,42 603.635.045.748,11 12,17 531.883.421.113,70 10,03 328.281.522.019,81 8,97

KALIMANTAN 6 93.906.401.358,81 3,45 133.815.377.758,41 2,95 149.387.547.412,72 3,01 145.569.713.232,88 2,74 104.545.532.389,01 2,86

UJUNG PANDANG 7,1 9.998.687.306,13 0,37 14.309.877.712,16 0,32 28.011.044.187,66 0,56 18.133.049.141,91 0,34 14.967.907.539,57 0,41

OTHERS 7,2 82.776.302.886,72 3,04 63.964.935.165,61 1,41 157.353.112.975,16 3,17 199.915.704.135,32 3,77 180.437.299.972,58 4,93

SULAWESI 7 92.774.990.192,85 3,41 78.274.812.877,77 1,73 185.364.157.162,82 3,74 218.048.753.277,23 4,11 195.405.207.512,15 5,34

OTHER ISLANDS 8 98.770.459.038,96 3,63 708.890.482.579,94 15,65 161.293.930.397,87 3,25 195.928.887.334,00 3,69 280.807.942.242,74 7,67

2.721.364.853.486,11 100,00 4.530.325.865.990,32 100,00 4.959.132.855.393,97 100,00 5.304.716.917.143,37 100,00 3.659.205.847.101,70 100,00

U/Y 2016

As At 31/12/2016

National Gross Premium By Cresta Zone

J U M L A H

U/Y 2015Cresta Zone

U/Y 2014


40

Detail Data



41

Detail Data


dalam Rupiah

U/Y 2012 U/Y 2013

Jumlah % Jumlah % Jumlah % Jumlah % Jumlah %

AGRIKULTURAL A 29.419.229.652,86 1,08 35.250.816.165,82 0,78 28.846.719.733,54 0,58 29.554.011.012,61 0,56 27.339.875.645,44 0,75

KOMERSIAL C 658.335.421.893,61 24,19 1.812.225.503.099,32 40,00 1.119.707.324.281,41 22,58 1.802.325.811.487,4

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