Leveraging Data Science and Analytics: Insights from Actuarial Roles
Explore the impact of data analytics on actuarial roles, conversion models, leads distribution, and collaboration across departments to enhance overall conversions and operational efficiency in the insurance industry. Learn how a multi-factor model can provide scientific conversion scores and optimize outcomes in this dynamic field.
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Presentation Transcript
10th Seminar on Data Science and Analytics 26 Feb 2022 Leveraging Data Beyond Traditional Actuarial Roles Ankit Mittal Associate Director Actuarial Policybazaar.com
Index Background on Conversion Model Leads Distribution Aims of the Model Factors and Model Form Outcome of Model Actuarial Model Giving Signal to Multiple Teams www.actuariesindia.org
Background on Conversion Model Conversion (Lead to Booking) was a function of basic factors / simple approaches Dominating factors were vehicle factors / rating factors while customer attributes were considered on the go Following points emerged: Conversions were increasing as planned but it was not clear if there is any opportunity being missed (i.e. can conversions increase further) Segmental conversions were not consistent www.actuariesindia.org
Leads Distribution (Sample Factors) Conversion increment at each level (Indexed to 1) Geography 1.09 1.08 1.07 1.07 1.04 1.04 1.04 1.03 1.03 1.02 1.01 1.00 1.00 1.00 1.00 0.99 0.98 0.97 0.97 0.97 0.96 0.96 0.94 0.93 0.90 0.89 A B C D E F G H I J K L M N O P Q R S T U V W X Y Z Health Customer NCB 1.26 1.2322 1.19 1.15 1.12 1.00 1.0000 0.98 0 20 25 35 45 50 No Yes Term Customer Vehicle Age 1.171.23 1.21 1.191.15 1.21 0.901.00 1.011.06 1.050.96 1.00 0.830.760.69 0.39 0.17 www.actuariesindia.org Yes 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 No
Leads Distribution on Factors What it Showed Some traditional factors like RTO were less significant Other traditional factors like vehicle age showed strong outcomes PB Specific / Digital factors showed a very strong outcome It was identified that a multi factor model may be built to provide a scientific conversion score to each lead A GLM based approach was then implemented to achieve multiple objectives with a primary aim to increase the overall conversions www.actuariesindia.org
Aims of the Model Overall conversion increase through collaboration of all departments Conversion increase to 25% + levels Reduction in time to convert the lead How it helps in giving signals to multiple teams : Marketing: Sourcing of leads Operations: Where is the need for more assistance BD: Signals to ops on customer preferences Actuarial: Build, operate, track and refine the models to achieve desired outcomes www.actuariesindia.org
Conversion Model Factors and Model 22 Factors GLM model PB/Online Specific Customer Behavior Car Specific Model Interactions 19.No. of visits on website 20.No. of connected calls 21.Voice to text analytics 22.No. of time Claims word used in 5 minutes 1. Make/Model 2. Fuel 3. Transmission 4. CC Range 5. NCB 6. Geography 7. Vehicle Age 8. IDV 9. Plan Type 10.Date of lead 11.Advance renewals 12.Prev. Health Insurance 13.Prev. Life Insurance 14.Prev. Travel Insurance 15.Lead source 16.Plan Type/Vehicle Age 17.NCB / Vehicle Age 18.Plan Type/ Geography Extended model for PB renewals includes NCB history, payment mode, interaction between claim and time taken to purchase Modeled data includes more than 5million leads hence established strong data credibility www.actuariesindia.org
Outcomes of GLM Model Share of leads at each conversion probability 10% 9% 8% 8% 7% 6% 5% 4% 4% 1%2% 2% 2%2%2% 2% 2% 2% 2% 2% 2% 2% 2% 2% 2% 2% 1% 1% 1% *Sample Numbers It was found that there is 25% traffic that can convert with more than 20% chances Overall conversion was below 20% Model aimed to bring overall conversion to 25% + levels This would have brought PB to one of the highest customer conversion companies across all Fintech Companies www.actuariesindia.org
Outcomes of GLM Model Live model gave more than 100% increment on conversions Experiment vs Rest 26.34% 24.18% 23.18% 20.93% 19.41% 16.16% 11.42% 11.30% 7.92% 7.86% 7.16% 6.13% Experiment Rest *Sample Numbers Model was made live on sample leads to track its effectiveness Across each factor model resulted in more than 2x impact www.actuariesindia.org
Outcomes of GLM Model Actuarial Model is a Statistical Outcome Usage of it is an Art Operations: Signal available where assistance is needed and where lead can left unassisted Signal for customer preferences is available PSU/Pvt; Comp/Nil-Dep Marketing: Signal available for intelligent sourcing of leads Cost peer lead now directly correlated to outcomes of actuarial models Actuarial: Continuous monitoring of actual vs. expected Ongoing refinements to model and expansion of model to other LOBs Overall P&L Impact: Leaving those leads unassigned which have high chances of converting unassisted cost reduction Assistance showing higher conversions agent costs are fully justified as more conversion happening due to right lead allocation www.actuariesindia.org
