Session 5A, Identify Drivers of Company Value Using Data Analytics - PDF document

Session 5A, Identify Drivers of Company Value Using Data Analytics Presenters: Kin Tse SOA A Anti titr trust Disclaimer imer SO SOA A Presentatio ion D Discla laime

Identify Drivers of Company Value Using Data Analytics KIN TSE Data Scientist 18 June 2019

Speakers and their industry experiences Business Data Scientist Development Manager Financial Analysts Data Scientists a Business and Business and Subject Finance Finance matter knowledge knowledge knowledge a Data driven Data Flexibility driven a Data driven, Flexibility and automation and automation a accuracy 2

Swiss Re’s value proposition Teams in the Business and Analytics domain work together worldwide to provide solutions to the clients > 200 experts in Business Development (BD), Structured Solutions (SS) and Digital and Smart Analytics (DSA) teams Origination and structuring along with smart analytics capabilities to provide bespoke reinsurance solutions for both P&C and L&H Munich Beijing Toronto Tokyo Zurich London New York to provide client specific business solutions and services Hong Kong Miami Generate Bangalore Acclimate to sustainable financial Singapore changes in industry and strategic value Sydney Cape Town Insight Decision Support Generation Visualization BD & SS BD, SS & DSA 3

Swiss Re’s value proposition Today we will cover … Teams are fully embedded in Swiss Re’s Client Markets and Solutions organisation 1. Approaches by the Financial Analysts vs Data Scientists when identifying drivers of company value 2. Key themes in 2019 for Asia and their emergence 3. How technology and analytics are helping to make better data driven decisions that impact valuation of insurance companies 4. Case studies on how smart analytics influence risk management decisions including business steering and risk strategy 4

Swiss Re’s value proposition Financial Analysts take on valuing a company Teams are fully embedded in Swiss Re’s Client Markets and Solutions organisation 1 Industry overview 2 Market trends 3 Company highlights Growth outlook Regulatory changes (IFRS17, RBC, C-ROSS) Product portfolio and performance Impact of macro environment Competitor landscape Growth strategies Capital requirement Current margin and sustainability Corporate governance 5

Swiss Re’s value proposition How Data Scientists view a problem statement? Teams are fully embedded in Swiss Re’s Client Markets and Solutions organisation 1 Ideation phase Business case development • Feasibility assessment • Unstructured data Structured data 2 Validation phase Predictive modelling Machine learning Proof of concept • Deep learning ... Text mining Prototype • Visual analytics Big Data analytics 3 Using platforms: Factoring Phase Pilot • Production • ADAPT Insights Re DS Workplace Pythia 6

Swiss Re’s value proposition Key themes for Asian life insurers in 2019 Teams are fully embedded in Swiss Re’s Client Markets and Solutions organisation Number of discussions with clients related to InsurTech 400 300 200 100 Mortality Regulatory changes 0 2013 – single most important 2014 IFRS 17, CROSS, RBC 2015 protection gap 2016 2017 2018 2019 Projected Capital management Number of discussions with clients related to Data Science 100 80 60 Digitization of M&A 40 customer experience 20 0 2013 2014 2015 2016 2017 2018 2019 Notes: Projected (1) Market reports by Willis Towers Watson, EY and Deloitte (2) Using the count of discussions with clients on their strategies 7

Traditionally, company disclosures and market trading multiples are used to measure company performance 8

Swiss Re’s value proposition Leveraging smart analytics for market screening Teams are fully embedded in Swiss Re’s Client Markets and Solutions organisation Extracting insights from financial reports and enriching lead generation ahead of the competition Used smart analytics to convert unstructured data into user friendly templates to generate insights This boosted our lead generation capabilities to: gain insights into our clients’ business , and • understand the needs of our clients to prioritize our focus areas • Methods: Advanced Text Analytics Data: Financial Reports 9

Swiss Re’s value proposition Identifying the growth potential & profitability drivers in industry Teams are fully embedded in Swiss Re’s Client Markets and Solutions organisation Life insurers focusing on protection business in Asia are viewed favorably by investors Change in share of protection vs change in new business margins P/EV multiple vs EV growth forecast 3.8 80 Protection APE CAGR: 15-18 Insurers with 75 increased focus on 3.6 higher margin HDFC life 70 protection products 56% Ping An n Life fe CP CPIC C Life fe* 65 High valuation of AIA * ICI CICI Pru Pru HDFC & ICICI driven 60 39% 71% 1.8 by high market 55 growth potential and New Business Margin, FY15-18 (%) Price / Embedded Value (FY19E) 1.6 AIA* AI Prud udenti ntial l Plc lc (Asi sia) rapid growth in share 50 of protection products Manulife *** 1.4 *** 45 albeit from a low base 40 1.2 Ping An n Life fe** 18% 35 Samsun ung Life ife 1.0 30 CPIC CP C Life fe** 35% Hanw nwha Life fe 0.8 25 HDFC C Life fe Prudential Plc ^ Protection segment 20 Samsung Life China Life 0.6 gaining traction in a -12% New China Life 15 predominantly Max x Life fe TongYang Life 0.4 savings market 10 Hanwha Life Dai-Chi life Japan Post ICI CICI Pru ru 0.2 -10% 5 T&D Holding 0 0.0 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 -6 -4 -2 0 2 4 6 8 10 12 14 16 18 20 22 24 Estimated CAGR in Embedded value FY17 – 20E Protection Share as % of Annual Premium Equivalent / New Business Value, FY15-18 Notes: (1) AIA*: % change in share of protection premiums based on New Business Value between FY16-18 (2) Ping An Life, CPIC Life** : % change in protection premiums based on FYP i.e. First Year Premiums between FY15-1H18 and FY15-18 respectively (3) Prudential Plc^ : P/EV estimates from Credit Suisse for Global business; NBM margins calculated as New Business Profit / APE; Protection share FY15-17 (4) Manulife***: P/EV calculated as of 5 th April’19. EV does not include Wealth management, bank businesses, P&C and Reinsurance business. Manulife CAGR is based on 2 year historical growth rate of EV (5) Source: Company Annual reports, Company Presentation, UBS report, J.P Morgan report for P/EV, Credit Suisse reports, SNL, Bloomberg 10

Swiss Re’s value proposition How Senior Management can better monitor its KPIs Teams are fully embedded in Swiss Re’s Client Markets and Solutions organisation Swiss Re’s journey of monitoring and steering business KPIs from financial reporting to dynamic scorecard method Performance Management Financial Supplement to US GAAP Report EVM Report Performance Scorecards Plan the Plan How KPIs were reported to Current state of reporting Management in the past Performance Scorecard Dashboard Performance Scorecards for efficient and dynamic ▪ reporting of KPIs Automated data production with improved consistency ▪ Market view to provide a view closer to the ▪ organizational structure, to assign accountability and to track performance of Swiss Re’s markets. 11

With access to new sources of data, smart analytics are being used to develop solutions capturing changing our client needs 12

DSA Case Studies To gain deeper understanding of portfolio and drivers of 1 claims cost Anomaly detection to identify fraudulent behavior in 2 claims Property Risk Screener for getting insights from 3 unstructured data …and other use cases delivered 13

Swiss Re’s value proposition To gain deeper understanding of portfolio and drivers of claims cost 1 Teams are fully embedded in Swiss Re’s Client Markets and Solutions organisation For the last 2 years , loss ratio of a large life insurer was disappointing and only a general price increase helped to stabilize it. Cost and transition predictions for Business > 350’000 individuals However, the situation needed more sensitive and data driven pricing . The goal was Need to gain deeper understanding of the portfolio and to identify drivers of claims cost in order to outperform the plan for 2017 and 2018. Developed predictive models for: Reinsurance L&H i Analytics • Pricing – with insight about burning cost dependency on the structure of portfolio, Approach • Transition – to optimize premium changes with insight about price sensitivity and resulting lapses/package downgrades ✓ Improved loss ratio ✓ Review of whole portfolio Business In total, a 0.25% improvement in the loss ratio translates to around 1.5m USD reduction Impact in claims cost. 14