Machine Learning: making an impact

Transcription

1 Machine Learning: making an impact Tony Bradshaw Allianz, Regional CEO Benelux Willis Towers Watson P&C European Forum

2 This talk in a nutshell ML in a world of Change As ML advances, the value of people increases (THE biggest management challenge) Where s the money? Use cases First, evolutionary steps. Transformational and disruptive cases approaching From Best Practices to New Practices: Reinvent Not just for insurers, and that might be a concern for some and for others, a necessary competence to access market opportunity Paradox: Main purpose of the new sophistication is to radically simplify 2

3 Yodabytes Where are the people / managers / leaders? Business Response Not just for insurers Towards ZOFAI Change Where s the money? Where s the opportunity? Disruption/Dislocation 3

4 The trouble with change Perhaps there is more than one destination The unit cost challenge The two-squares challenge Scalable models bring new competitors Staying viable the real constraint 4

5 The two squares problem 1 2 Constraints Any attempt to reorient the organization hits constraints. How to solve this? 3 1 Relax the constraints (e.g., by increasing the CTC budget and capacity). 2 Increase organizational agility / simplicity Company tomorrow Company today 3 Workforce and Cultural transformations are required to create an organization that is able to adapt irrespective of the constraints: from 2 squares to Circle within a (smaller) square over time. 5

6 The surprises have started (man-computer symbiosis) First game of chess (1997) Second game of chess (2005) Garry Kasparov lost to Deep Blue To many, this was the dawn of a new era, one where man would be dominated by machine Look at the podium: 1. Amateurs + PCs + process 2. GM + Supercomputer 3. GM Weak human with PC but a better symbiosis beats even a Grandmaster with supercomputer 6

7 New questions: man-computer symbiosis has achieved amazing results FoldIT a game that saved human lives 9/11 Meaningful adjacency wonder Big Data Use Cases Digitalisation Anti - Terrorism Security & Privacy Disaster response Health & Wellness 7

8 Advanced Analytics (ML) is only one part of the response building a Data Office DVM Launch DVM Team Collibra Set up Launch Data Governance TOM Enable GDPR Go Live Data Culture Key Milestone Completed Milestone For validation MDM Define an MDM strategy Data Profiling dashboard Value Monitoring Dashboard Implement Data Quality Framework Take Off Set up the Agile SWAT Team & Tools CGP Iteration 1: Customer CGP - Iteration 2: Product & Broker Benefits Measurement CGP Next iterations Our destination AA Setup of Advanced Analytics Reusable AA Clusters 3 day Master Training Pilot first Use Cases Industrialize first Usecases Adv. visualization tool selection Data Integration Supply Chain Dashboarding implementation BI ABC Onboarding Change TOM to agile Data Architecture 8

9 Wave 1 of potential use cases Approach for prioritization Ran workshops with the business heads, i.e., Life, Distribution, Claims, Non- Life Retail, Mid Corporate Identified key business priorities Identified potential use cases Assessed each use case vs. feasibility of implementation and potential impact, leading to a first ranking of the most relevant initiatives Assessment of AA applications Implementation feasibility 1 High Medium Low 4G Identify potential clients with low risk profile whom to sell life protection products Improve technical 4H pricing of Life Protection products Identify best clients within EB contracts whom to upsell / cross-sell guarantees / products 2B Extend time series of internal data and complement with external data sources to better price customers that re-enter the ptf Use external data to better price Health risks 4F 3A Identify clients to upsell other guarantees on top of properties/liability 4C 3I Identify customers to whom to offer reinvestment of maturities 2E/F Low EUR 5 m Medium EUR 20 m High Other use cases not possible to assess - and not identified as first/2017 priority during the workshops 1E Use of Telematics data to identify pattern/insights on claims to be then used for all Motor portfolio 2G Use of IoT data to improve UW/pricing, i.e., prevent claims, offer additional services 1D 2D Identify and prioritize brokers with higher expected performance Health claims automation and bestrouting Identify existing clients whom to upsell UL and protection product 4B Improve UW/pricing of specific sectors with additional data (tourism) 2F 3L Use external data to better price EB 2B 4E Simplify the process of collecting data for UW/pricing for Non- Life Retail and Imprese Area 1 Claims 2 SME/ MidCo 3 Non-Life Retail 4 Life 5 Distribution Estimated value at stake 2 5D 2A Increase pricing/p&c granularity of Non-Life Retail Identify new clients with high propensity to buy Identify clients whom to offer additional non-life coverages (beyond property and liability) Priorities based on workshop discussion 5E 3B 5C 5A Best matching of loss assessors, experts etc. Monitor performance of agencies in order to prevent PTF transfer Identify customers to focus retention actions 1B Predictive estimation of claims most likely to be subject to fraud 5B 4A Provide the agents with suggestion on the max level of discount allowed (to reduce the use of discounts) 3C Identify clients whom to offer the best next product to buy Identify best customers to convert from traditional to UL Priorities based on assessment Reduction of Bodily injuries cost of claims 1A through predictive estimation of cost of claims Improvement of technical pricing in Non-Life, incl. use of telematics, nonlinear methods, external data, etc. Fully exploit digital data (segment customers and use telematics data to offer additional services; use data from digital (home insurance, etc.) to assess customer behaviors on multi-channel 1C 3E/F 1 Dimensions: data and modelling, workflow integration, adoption 2 Annual run-rate bottom-line impact based on external benchmarks 9

10 Relativities Exposure (%) Use case: motor pricing (risk premium model, effect on top of all traditional factors) Relativities Driving Score Exposure Model Prediction at Base levels GLM built on the overall ptf and then adapted to the TMX ptf (all factors in offset) Driving Score levels 10

11 New products and services Advances in data science and medicine enable new transformative products and services Long-term Risk profiling based on medical histories Outcome-based patient steering In-population medical trials Short-term Precision medicine Immediate Future disease prediction Cost-based patient steering Medical leakage Specific disease markers 2 nd opinion conversion Drug interactions Drug-disease relationships Data availability 11

12 Towards a Zero Ops, fully Artificial Intelligence Model Main Idea Approach based on: operating model reverse-engineered, starting from customer (CX driven) small, (agile and commoditised) IT and process footprint commoditisation and outsourcing model ensures best tech stack is always available hyper-simplified processes and products instantaneous admin radically lower (unit) cost curve focus of ML and AI: leverage specialised data and E2E understanding/analysis of sales and marketing, U/W processes, risk prevention replace cumbersome business rules and radically simplify customer-visible pricing and U/W practices GDPR and putting customer in control of their data to build trust Two different types of market opportunities: reinvent traditional LoBs and products create business models for emerging market opportunities Is a Zero back office possible? 12

13 How to define your Minimum Viable Product? 13

14 Where s the opportunity? B 2 C B 2 B 2 C Bplex 2 C B A Z B C B B B 14

15 Making an impact (summary) Change, choices and strategy Picasso and computers Why all the statues? Reinventing insurance the people agenda has never been more important 15

16 Follow me on aj_bradshaw_photography 16