Information about a seminar (PDF)

Den Norske Aktuarforening inviterer til seminar om «Reserving – Machine learning and actuarial transformation» i regi av fagkomité skade. Invitasjonen kan i sin helhet leses her.

Tidspunktet for seminaret er mandag 28. mai 2018 fra kl. 09:00 til kl. 16:15.

Stedet for seminaret er Felix konferansesenter, Bryggertorget 3, Oslo.


09:00 – 09:15 Welcome and introduction v/ Mats Sollie, leder fagkomité skade

Machine learning and claims reserving

09:15 – 11:30  ‘ASTIN working party: Individual Claim Development with Machine Learning’  v/ Salma Jamal, Actuary, KPMGPresentation of the results of the ASTIN working group that focused on the application of machine learning methods in non-life reserving.

11:30 – 12:30  Lunch

Actuarial transformation

12:30 – 14:00  ‘7 days to 7 seconds: Process Industrialisation’ v/ Adrian Ericsson, Director, Dynamo Analytics

How can you condense your financial and statistical processes from 7 days to 7 seconds? Adrian will discuss the increasing pressure for greater complexity, speed and transparency of modelling, the consequences of the standard response and the need for a fundamental rethink.

14:00 – 14:15 Pause

14:15 – 15:45 ‘Actuarial transformation: the route to IFRS17 smart compliance?’ v/ Alice Boreman, Global Actuarial Solution Lead, QBE Insurance

A high level over view of why IFRS17 drives more from actuaries, historical lack of investment in Actuarial transformation and what a future state could look like.

15:45 – 16:15 Questions & Answers/ Panel Discussion / Summing up   v/ Salma Jamal, Adrian Ericsson, Alice Boreman, Mats Sollie


Seminaret, inkludert lunsj, koster kr 2.500,- for DNA medlemmer, kr 500,- for aktuarstudenter, universitetsansatte og DNA pensjonister og kr 3.500,- for øvrige.


Bindende påmelding innen 20. mai på , eller til sekretariatet tile-postadresse [email protected].

Ved påmelding, oppgi: navn, selskap, adresse, telefonnummer, e-postadresse og om du er medlem av DNA. Prinsippet ”førstemann til mølla” blir benyttet.

Om foredragsholderne:

Salma Jamal 

Salma JAMAL (B.Sc in Maths, M.Sc. in Actuarial Science) is a qualified actuary of Institut des Actuaires. Salma worked in the R&D department of NATIXIS Assurance on Mortality and Lapse modelling subjects. Actuarial consultant within KPMG since 2014, she is involved in several topics: projection models, MVBS and reserving processes audit, data government. Salma was laureate of a competition organized by Institut des Actuaires and presented an actuarial paper on lapse modelling at the 2016 ASTIN colloquium. She contributed to a paper presented at the 2017 ASTIN colloquium on non-life claims development with Machine Learning methods. To date, she is involved in various projects on Machine Learning (ASTIN working group)

Adrian Ericsson

Adrian co-founded Dynamo Analytics after a 15 year career building and implementing actuarial and statistical models for global insurance companies. A qualified actuary with diverse practical experience covering capital, reserving, pricing and risk management, Adrian has established and led actuarial teams in both the UK and South Africa and sat at executive level in both countries. Adrian focuses on the modelling of capital requirements and business optimisation and has led engagements to build, embed and use actuarial models across a variety of the insurers. Previous roles include Chief Actuary for Argo International, UK Head of Capital for Swiss Re and Capital Modelling and Solvency II expert at Deloitte.

Alice Boreman

Alice joined QBE Insurance last year to lead their global actuarial transformation, with a core focus on smart IFRS compliance. She is also a member of the IFoA IFRS 17 for GI Working Party. Alice specialises in large scale finance and reserving transformation projects. Prior to joining QBE, she was a core part of Deloitte’s IFRS17 proposition, regularly in discussions with their clients about the practical implications of IFRS17, performing IFRS 17 impact assessments and supporting the development of Deloitte’s in-house tools.

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