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Transforming Insurance Operations through Data and Analytics

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Transforming Insurance Operations through Data and Analytics

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Analytics and big data is established in Insurance could be better. It's not joined up. Big data is an accelerator of what is possible.

Roger Oldham of Amethyst Business Consultancy explains the impact of big data and analytic technology in the Corporate / Wholesale Insurance market.

Analytics and big data is established in Insurance could be better. It's not joined up. Big data is an accelerator of what is possible.

Roger Oldham of Amethyst Business Consultancy explains the impact of big data and analytic technology in the Corporate / Wholesale Insurance market.


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Transforming Insurance Operations through Data and Analytics

  1. 1. www.datalytyx.com Transforming Insurance Operations through Data and Analytics Roger Oldham
  2. 2. www.datalytyx.com A day in the life of ……..
  3. 3. www.datalytyx.com About Speaker: Roger Oldham • Founder and Managing Director of London Market Technology Exchange; London Market People. • 28 years in the London Insurance Market. Industry change and modernisation specialist. • Ex-Head of Claims and Head of Market Practice positions in Aon, HSBC Insurance Brokers and Marsh. • Qualified Mediator of the Chartered Institute of Arbitrators in London and Fellow of the Chartered Insurance Institute. • Non-Executive Advisor to Datalytyx Roger Oldham BA(Hons) FCII MCIArb FInstLM
  4. 4. www.datalytyx.com Objectives Today 1. Analytics in Insurance State of market Readiness Uses per sector of insurance 2. Compliance and Regulation Challenges 3. Other challenges re data “Accidental Architecture” Data access challenges
  5. 5. www.datalytyx.com My sector definitions for InsurancePersonal/Retail • High volume, low value • Life, Health, Home, etc. • Composite providers Corporate/Wholesale • Low volume, high value • Marine, aviation, property, casualty • Insurance binders Reinsurance • Risk transfer and arbitrage • Treaty reinsurance • Facultative Reinsurance
  6. 6. www.datalytyx.com Insurance Market State of Readiness for Big Data & Analytics
  7. 7. www.datalytyx.com 86% of insurance CEOs believe technological advances will transform their businesses in the next 3 to 5 years – more than any other factor. Unleashing the Value of Advanced Analytics in Insurance – McKinsey (2013) “Innovations in analytics modelling will also enable carriers to underwrite many other emerging risks that are underinsured including cybersecurity and industry-wide business interruption stemming from natural disasters.” Lots of commentators…….. “Big data will undoubtedly be the thing that will reshape our industry.” 17th Annual Global CEO Survey - Key Findings in the Insurance Industry PWC February, 2014 Can Reinsurers Ignore Big Data? – Bryan Joseph, Partner and Global Actuarial Leader, PWC, March, 2014
  8. 8. www.datalytyx.com Global Data Analytics Survey (for Insurance), 2014, PWC Infographic - Global Data & Analytics Survey 2014 (for Insurance) PriceWaterhouseCoopers, September 2014 Executives 71% yes - have changed the way they approach decision making as a result of data analytics 23% no , but plan to
  9. 9. www.datalytyx.com Uses Interesting No mention of analytics for compliance & regulation
  10. 10. www.datalytyx.com In summary …….. its multi-speed! ReinsuranceCorporate / WholesalePersonal/Retail
  11. 11. www.datalytyx.com Analytics distinctly different per insurance sector…..Personal/Retail • Fraud analytics and detection • Behavioural analytics • Geospatial analytics: property, weather, etc. • Affinity groups Corporate/Wholesale • Geospatial analytics: transit routes, flight paths • Political and territorial risk predictive analytics • Binders Analytics: per binder contract, per peril, etc. Reinsurance • Complex analytics e.g. probability distributions • Catastrophe modelling based on actuarial analytics • Distribution of outcome and contract pricing Compliance & Regulatory Analytics re: claims handling, complaints handling, solvency, liquidity, treat customers fairly, etc.
  12. 12. www.datalytyx.com Consider the challenges of compliance and regulation What are the specific challenges around: • Compliance • Regulations • Performance optimisation
  13. 13. www.datalytyx.com Regulation increasing………and it now has teeth!!! Regulators: FCA / PRA • Liquidity & Capital Adequacy • Solvency II • Transparency Framework • Claims Handling • Complaint Handling • Treating customer fairly • Changes in global risk profiles Fines • Swinton Group - £7.38m • Besso - £315k • Stonebridge International Insurance - £8.4m • Homeserve - £30m • Debeka (Germany) - £1.3m Challenges • Constantly evolving • Changing landscape • New demands • Existing infrastructure doesn’t often support • Added cost • Cant deliver the “evidence” without data analytics (or a ton of people)
  14. 14. www.datalytyx.com London markets …… • Lloyds is another regulator • Evidence adherence to business plan / policies • Ensuring meeting capital adequacy • Time consuming without data & analytics
  15. 15. www.datalytyx.com More than X number of complaints upheld against an adviser More than X% of business with one provider. X% of files reviewed revealed issues that require significant remedial action. Compliance / Regulation Measures Brokers Commissions Binder Activity Complaints Volume/Values Monitoring Underwriting Loss ratios Transparency Claims Solvency Performance Underwriting Claims Complaints Financial Domains
  16. 16. www.datalytyx.com So…… What are the practical challenges to delivering on Big Data & Analytics?
  17. 17. www.datalytyx.com Accidental Architecture … anatomy of an insurer 3rd Party Data Subscriptions Unstructured documents, emails Clickstream Server logs Sentiment, Web Data Sensor. Telematics Geolocation, Spatial Existing Data Infrastructure New Data Sources
  18. 18. www.datalytyx.com Challenges ……. • You need to physically get access the data • There are often ‘data guardians’ • You need a place to put it all • Data quality will be an issue (declarations)
  19. 19. www.datalytyx.com But what advice on solutions can a non-techy offer you?
  20. 20. www.datalytyx.com I like the “Data Lake” as a concept - C-suite does also
  21. 21. www.datalytyx.com Non-exec Advisor to Datalytyx Cloud & Managed Services for Big Data & Analytics Data Quality & MDM Enterprise Information Management Data Swiss Army Knife for Data Integration / Management Data Quality Big Data Integration
  22. 22. www.datalytyx.com Datalytyx Cloud – Big Data Analytics & Performance Datalytyx Cloud for Analytics & Performance Customer Data Sources PERFORMANCE / GOVERNANCE GOVERNANCE STRUCTURE AND DATABASE Scorecards Measures and KPIs Targets & Thresholds ▶ Sap ▶ Oracle ▶ Agresso ▶ Infor ▶ Concur CORE BUSINESS DATA SOUCES ▶ Remedy, HP, CA ▶ Tivoli, Openview, BMC ▶ Project/Portfolio Management ▶ Capacity and Utilization Systems ▶ Backup/Monitoring Systems ▶ Financial Systems ▶ Cloud Systems ▶ Customer Satisfaction Systems ▶ 3rd Party System ▶ Web Logs & Click streams ▶ Machine generated ▶ Geolocation data ▶ Marketing automation systems OTHER POTENTIAL DATA SOURCES ▶ (Spread Sheets) ▶ (CSV Export) ▶ (Text Delimited) OTHER FLATFILE SOURCES ▶ Salesforce ▶ Netsuite HIGH SPEED DATA ANALYTICS & DISCOVERY DATA CLEANSING / MANAGEMENT Custom Data Cleansing & Data Processing Rules Various Extracts Various Extracts Various Extracts Clean Data Data Lake of High Quality Data Action, Issue, Risk, Milestone, Service Improvement Tracking CSV HP Vertica High Speed Analytics Tableau Governance Scorecard Analytics Scorecard
  23. 23. www.datalytyx.com Datalytyx & Talend - Finance & Insurance Customers
  24. 24. www.datalytyx.com Thanks & Questions

Notes de l'éditeur

  • Upto roger – does he have a funny into to do – how does he want to start the session
  • Roger to describe himself
    Practitioner – not an actuary – or modeller

    Come from this from a business perspective – not a technical perspective (compliance and process perspective)

    Look at …..
    business needs for analytics across the various types of insurance
    not all modelling and predictive
  • Objective for today
    Take a business view of big data and analytics in Insurance
    Look at the challenges – c-suite demands and desires
    Consider what the c-suite wants from big data analytics based on what I am hearing

    Not a technical
  • Firstly
    We will refer to some sectors of insurance during this session

    Want to share with you my definition of the sectors

    Describe the slide briefly
  • So lets just checkpoint on the state of readiness

  • We all have a feeling this is already transforming the business of Insurance - BIG DATA is just an accelerator.

    And if anyone disputes it, all the commentators agree as a result of the survey they have performed.
  • PWC Survey for Insurance in 2014
    In 2014, big data and analytics had started to change the way the majority of insurance executives approach big decisions (71%)…

    The top three changes in the last 24 months include:
    More reliance on enhanced data analytics such as simulation, optimisation, or predictive analytics.
    Many organisation have employed dedicated data insights team to inform strategic decisions.
    Many changes to the way data or analytics is presented to management – more graphical

  • Analytics and big data is established in Insurance
    could be better
    Not joined up

    Big data is an accelerator of what is possible

    Think this needs to be changed to be a graph on adoption????
  • Discuss each of them - slightly differently.


    In all of these its about estimating risk and writing better business

    Personal/Retail & Reinsurance often more mature than corporate wholesale insurance
    I know this intimately as I have been working with them for the last few years
    Typically smaller business – same level of regulatory compliance – and massive opportunities for better analytics t o gain insights and write better business

    But there is 4th area – consistent across all of them - Compliance and Regulatory analytics (to deliver evidence to auditors when needed)

  • But lest look at the specific challenges around compliance and performance optimization and where analytics feeds into this with specific thoughts on the Corporate / Wholesale market. 
  • From a regulation perspoect
  • Lets start with KPIs –
    Helps us understand the domains of data required
    and what actual data is needed
    And therefore

    that will drive the systems we need to secure the data from

  • 1. Complex Environment
    Challenge is we have a complex environment; we all recognise the picture above! we have a lot of systems for discrete purposes and a lot of data
    Some of us will have managed to centralise and optimise everything into one place
    Most of us have not!
    Some have invested in a data warehouse – but they have cost a ton and in many cases have not deloivered

    Datalytyx and Talend call this the “Accidental Architecture”

    2. New Data Sources
    Then we have the additional new sources of data – often considered big data sources due to their nature and their volume

    So – how do we bring all of this together to enable the analytics demanded of us?
    Not just around core business analytics, but around compliance and performance and ultimately, transforming our business.
  • So in the previous slide we showed the potential complexity of an insures systems

    So what remain the challenges for analytics – given that system map as a background

    regardless, you still have to physically get the data – in whatever format you can, and this remain difficult – you need tools to do this
    There are often data guardians – they OWN their individual bit of data – and they will challenge you – so BE PREPARED – they are not to be underestimated
    And you will need a place to bring it all together – a central repository – not another EDW – will come back to this
    Data quality will often be questionable – and you will need to cleanse and transform at data

    And probably many more issues

  • I like the concept of data lakes – its not technical - its understandable
    - Like the fact that data can be quickly poured into them with little fuss or muss, and they're relatively low cost to operate, unlike data warehouses.

    C-suite like Concept of a data lake - hits home – and they like the fact its NOT an EDW and the price point is much lower

    I'm sure its not as easy as everyone says
    - but I know the guys at Datalytx and Talend are delivering one for AstraZeneca and its not as difficult as an Data Warehouse

    But what if you don’t have exec buy in yet
    - Why not build well-designed data puddle, that can easily grow is more likely the better approach
    - choose a use case (and I would argue compliance may be the one as it needs multiple domains of data) and use that to justify the creation of the data lake.

    Reading for Roger
    http://www.insurancenetworking.com/blogs/do-you-really-need-a-data-lake-in-your-back-yard-34444-1.html (some reading for roger)

    Data lakes are part of the Apache Hadoop ecosystem, serving as low-cost repositories for data of all types and sizes. Since data can be quickly poured into them with little fuss or muss, they're relatively low cost to operate, unlike data warehouses, which require ETL, cleaning and normalization of data.

    What's the advantage of data lakes to insurance companies? Much of the data that is valuable to the policyholder application and claims administration processes is based on a lot of unstructured data: notes from agents, call center notes, photos of properties before and after damage, sensor data from telematics, geospatial data and social media data, just to name a few. The ability to put all this information together, vs. out in separate systems, such as content management, policy administration, and so forth, may enable faster access, at lower costs.
  • I am non-exec advisor to Datalytyx who work hand in hand with Talend

    Talend is all about providing a swiss army knife for data
    …and Datalytx is all about how best to use the knife and make best use of all these other great technologies
  • They work with a ton of customers in the insurance sector around

    Enterprise Information Management
    Big Data & Analytics

    And the have some very interesting and unique delivery approaches. Why not say hello to Justin and Tim and chat further with them.