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Data Driven Decision Making

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Presentation on theme: "Data Driven Decision Making"— Presentation transcript:

1 Data Driven Decision Making
Hatim Maskawala October 19, 2017

2 How many of you believe that investing in Social Media is the way forward And how many companies have kept this as part of their strategy and have actually done something?

3 Facebook Analysis - Insurance

4 Facebook Analysis - Insurance

5 Facebook Analysis - Banks

6 Insurance vs Banks

7 Underlying Forces An explosion of data and contents
Reliance on external data An explosion in computing power The rise of information activism Self service BI There is now more and more data available with us The processing power of systems have gone up tremendously The new generation has been brought up using data and computers

8 Challenges No single view of customer
Ineffective customer segmentation Multiple distribution channels High volumes of fraud, wastage and abuse Most insurers in MENA rely on policy admin systems for analytics Underwriting vs Financial/Accident years Data is segregated across systems Manual data / excel files Many calculations are in excel Need for Customization Requests

9 Analytics Maturity

10 Descriptive Analytics
The rearview mirror is descriptive analytics. Its based on past data and shows where you have been.

11 Predictive Analytics

12 Diagnostic Analytics The rearview mirror is descriptive analytics. Its based on past data and shows where you have been.

13 Maximizing analytic value
HIGH Predictive analytics Analytic value Diagnostic analytics Descriptive analytics (What is likely to happen?) LOW (Why did it happen?) Reporting and viz tools BUSINESS USERS DATA SCIENTISTS Need to combine all in one tool

14 Difference Between Flat Reports and BI
Flat reports give too much information about what happened. Used for transaction BI tells WHY it is happening Allows you to interact with the data Answers your next question

15 Difference Between Flat Reports and BI
Criteria Operational Reports Business Intelligence Business Function Tactical Analytical Users Staff Executive Management, Analysts, LOB Heads Data Sources Core system Only Any including core e.g. TPA Data Mode of Operation Query then Analyze Analyze then query (on the go) Type Static Dynamic KPI Measurement No Yes Real Time Near Real Time Analysis Across Subject Areas Yes e.g. Combined Ratio (as it has expenses, etc)

16 DAR Concept Dashboard High level KPIs
Something which gives you an overall picture in two minutes Analysis Has graphs and tables and tells the users “how” or “why” Report Transactional level information Has more details like policy details, claim details

17 Using Analytics Product Development Production Performance Servicing
Reinsurance Finance Using Analytics

18 Using Analytics Product Development Production Performance Servicing
Reinsurance Finance Using Analytics

19 Product Development Health/Motor insurance has become extremely data oriented Pricing / decision making should be analytics driven. Always validate perceptions with data Start at the top and drill down Pricing factors and pricing tool Use of pricing tool will provide standardised rates Medical pricing is highly data dependent and here due to eclaims we have lots of data. Some general principles that we all should remember and this is not only for actuaries but for all: Data driven. Don’t base your conclusions on perceptions. We cant give this cover what if customer goes to US and Canada and gets it treated there? We have given this cover in the past and how many have gone? Always look for evidence from data to validate or invalidate perceptions / hypothesis. Don’t spend time on trivial things. Like in medical IP / OP and Mat generally make up 90% of the claims. So don’t spend hours evaluating alternative medications or optical etc. Start at macro level. Like start with OP and then start breaking it down. Rather than worrying about 8000 CPT codes and pricing for each. Data will give you facts. You need to have a plan to benefit from those opportunities. Like data might show that pharmacy costs fall by 40% when we apply 20% coinsurance. So the plan should be that we will give 30% discount for 20% coinsurance and push this as an option for all quotes. The feedback loop is required in all cycles. Routinely do back testing and reassess your hypothesis in light of actual data after you had made the hypothesis. In pricing do not forget the pareto 80/20 principle. Don’t spend majority of your time on pricing riders.

20 Deeper Pricing Analysis

21 Deeper Pricing Analysis

22 Using Analytics Product Development Production Performance Servicing
Reinsurance Finance Using Analytics

23 Sales - Client Client segmentation
By size – analyse your existing portfolio to find out which segments are profit making By industry – generally difficult to evaluate. Can be done as part of data cleaning or one time exercise Campaign optimization / lead generation Where to cross sell What to upsell

24 Sample Production

25 Using Analytics Product Development Production Performance Servicing
Reinsurance Finance Using Analytics

26 Sample Health Performance

27 Sample Motor Performance

28 Loss Ratio by Body Type and Sum Insured Bands

29 Performance Monitoring

30 Analysing Target Segments

31 Using Analytics Product Development Production Performance Servicing
Reinsurance Finance Using Analytics

32 Customer Service Customers are less loyal and more price conscious
Insurance managers are looking for more insights / advices Clients wants transparency / more analytical reports Need proper analytical tools which can handle big data Previously it used to be just print cards. Now they want detailed reports. Health premium is growing year on year and impacting their budgets. A company with 5,000 was paying AED 1,100 per member and now you quote them AED 1,400 per member. Their costs have increased by AED 1.5 mn. When you say loss ratio was bad their reaction is that its your job to maintain the loss ratio. The larger customers have stopped trusting insurance companies and want constant report. Many time staff of insurance companies spend time in assembling the report and not assessing them.

33 Use of Systems for Fraud
Complex Probab-ilistic Rules Calculate scores for claim Calculate scores for patient based on past history Determ-inistic Rules Rule edits Diagnosis / treatment rules Statistical Reports Distribution of claims by provider By diagnosis / treatment Standard Reports Utilization per provider Provider KPIs Averages per diagnosis Historical / Retrospective Predictive / Real Time Historical data / experience will help create new rules and setup complex probabilities.

34 Gap within Consultations

35 Normal vs C-Section

36 Rank Analysis on Drugs

37 Using Analytics Product Development Production Performance Servicing
Reinsurance Finance Using Analytics

38 Risk Accumulation Visually
We can show accumulation by geography also and the spots would be updated based on selections. The size and colour can represent different dimensions.

39 Risk Accumulation Reports

40 Using Analytics Product Development Production Performance Servicing
Reinsurance Finance Using Analytics

41 Automated Financial Reports

42 Financial Loss Ratio

43 Are you data driven Data Denial You distrust data and avoid using it Data Indifferent You don’t care about data and have no need for it Data Informed You use it only when it supports your opinions or decisions Data Driven You use it to shape and evaluate all your decisions This is one time cost that reaps ongoing benefits. No use reinventing the wheel To become a Data Driven Company it has to come from top

44 CONTACT 2107 SIT Towers, PO Box 341486, Dubai Silicon Oasis,
Dubai, UAE Phone: Fax:


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