How Machine Learning Can Bring the Fight to Fraud, Caear Baulona
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Present by Caesar Baulona at the 4th Annual East Africa Finance Summit How do we effectively sort incoming claims so that we most efficiently allocate resources to obtain additional information at a cost?
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How Machine Learning Can Bring the Fight to Fraud, Caear Baulona
- 1. How Machine Learning Can Bring the Fight to Fraud 4th Annual East Africa Finance Summit Caesar Balona FASSA
- 2. | 2QED Actuaries & Consultants Framework Strategy Detection Machine Learning
- 3. How do we effectively sort incoming claims so that we most efficiently allocate resources to obtain additional information at a cost? A fundamental problem exists...
- 4. | 4QED Actuaries & Consultants Framework Strategy Detection Machine Learning
- 5. | 5QED Actuaries & Consultants QED’s Fraud Detection Framework StrategySystems&IT Screening & Detection Monitoring Operations&HumanResources Governance Continuous Improvement
- 6. | 6QED Actuaries & Consultants Framework Strategy Detection Machine Learning
- 7. | 7QED Actuaries & Consultants Framework is built around the following goal: Efficiently and Effectively Identify Fraud Allowing resources to be efficiently utilised by focussing efforts and maximising success rates Ensuring resources are used effectively by monitoring and measuring results against objectives Providing robust statistical processes that identify claims that are highly likely to be fraudulent 𝑮𝒐𝒂𝒍 = 𝑚𝑎𝑥 𝑣𝑎𝑙𝑢𝑒 𝑜𝑓 𝑓𝑟𝑎𝑢𝑑 𝑑𝑒𝑡𝑒𝑐𝑡𝑒𝑑 − 𝑐𝑜𝑠𝑡 𝑜𝑓 𝑑𝑒𝑡𝑒𝑐𝑡𝑖𝑛𝑔 𝑓𝑟𝑎𝑢𝑑
- 8. | 8QED Actuaries & Consultants Measuring the value of an improved fraud management system 𝑽𝒂𝒍𝒖𝒆 = 𝑎𝑑𝑑𝑖𝑡𝑖𝑜𝑛𝑎𝑙 𝑣𝑎𝑙𝑢𝑒 𝑜𝑓 𝑓𝑟𝑎𝑢𝑑 𝑝𝑟𝑒𝑣𝑒𝑛𝑡𝑒𝑑 − 𝑎𝑑𝑑𝑖𝑡𝑖𝑜𝑛𝑎𝑙 𝑐𝑜𝑠𝑡 𝑜𝑓 𝑖𝑛𝑣𝑒𝑠𝑡𝑖𝑔𝑎𝑡𝑖𝑜𝑛 − 𝑜𝑛𝑐𝑒 𝑜𝑓𝑓 𝑐𝑜𝑠𝑡 𝑜𝑓 𝑝𝑟𝑜𝑗𝑒𝑐𝑡 Increases due to: - Improved success rate - Faster processing - Increased capacity Increases due to: - Increased capacity But, also decreases due to: - Increase efficiencies Cost of: - Implementation of the Framework - Operational expenditure
- 9. | 9QED Actuaries & Consultants Framework Strategy Detection Machine Learning
- 10. | 10QED Actuaries & Consultants Standard selection process Reported Claims Payment Analysis by Investigators Repudiate
- 11. | 11QED Actuaries & Consultants A number of problems arise Large number of claims per investigator Claim growth outpacing investigation potential Too many claims Low number of claims investigated Very low proportion truly fraudulent Low chance of success Process static and learning centralised in few experts Limited investigators, unclear business case for more Limited feedback loop Process relies on subjective expert judgment Experts change and judgement varies No statistical basis
- 12. | 12QED Actuaries & Consultants Moving toward an efficient, robust system for the selection of claims Reported Claims Screening high suspicion Payment Repudiate Analysis by Investigators fraud Model Architecture
- 13. | 13QED Actuaries & Consultants Screening provides a high level process optimisation Remaining claims sent to a target model based on rules Claims are sent to target destinations based on business rules Claims enter screening module Screening Model Architecture Model A Model B Model C, etc Immediate Investigation Immediate Payment Immediate Repudiation due to T&Cs Re-enter Screening Claims
- 14. | 14QED Actuaries & Consultants Column Column Suspicion score Data Data 99% Data Data 95% Data Data 80% Data Data 60% Data Data 40% Data Data 20% Data Data 0% Reported claims The Model Architecture optimises the list of possibly fraudulent claims The claims can then be sorted by suspicion of fraud, and the top most few investigated Claims are given a statistical suspicion score output by the fraud system
- 15. | 15QED Actuaries & Consultants The process improves iteratively over time, learning and adapting Claims data with fraudulent claims explicitly labelled Fraud system New list of fraudulent claims detected Investigation of detected fraudulent claims Expanded subset of labelled fraudulent claims and non- fraudulent claims Performance WastedResources
- 16. | 16QED Actuaries & Consultants Framework Strategy Detection Machine Learning
- 17. | 17QED Actuaries & Consultants (Some) Approaches to detecting fraud Logistic Regression Decision Trees Neural Networks + Simple structure + Quick to implement - Linear - Rigid + Relatively simple + Still quick to train and optimise - Rigid + Highly customisable + Non-linear - Complex - Difficult to train and optimise
- 18. | 18QED Actuaries & Consultants Conclusion A robust framework that encompasses all key operations of fraud detection is crucial. Fraud detection is not just a good model Framework A robust strategy that sets measurable goals and objectives is required. Value add must be measured to understand the benefit Strategy Detection models must be continuously learning and improvingDetection Numerous models available with increasing complexity meaning there is an access point for allMachine Learning
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