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© 2021 IBM Corporation
Processing Insurance Claims with Automated, Scalable and Fair AI
Maxime AllardData Scientist, IBM Data Science & AI [email protected]
Kristen McGarryData Scientist & Industry CTO, Financial [email protected]
Kenneth LimSenior Data Scientist, IBM Data Science & AI [email protected]
July 22, 2021
Agenda
• The new horizon of claims management
• Infusing AI across the process
• Deep-dive into our latest accelerators
• Successful case studies
• Q&A
2
Acquisition
• Digital channels & online marketplaces
• Retail Agents, Brokers, advisors
Capture, classify, and extract data from
applications
Automate u/w decisions with business rules
Design and manage end-to-end workflow
Securely manage content
Automate repetitive tasks with RPA
Improve operational Intelligence
Automate with low code / no code tools
Policy Issuance• Digitized policies, riders,
terms and conditions
• Predictive underwriting models against claims data
IT Management
• Proactive incident management resolution
• Enforcing policies & cost controls across cloud
Invoicing & Billing
• Modern digital payments
• Optimized transactions, across channel ecosystem
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2 3
© 2020 IBM Corporation 3
Provide an enhanced customer experience throughout customer journey
Improve and optimize with Process Mining
Manage API lifecycle
Integrate with rating and underwriting
systems
Claims / Service• Contactless mobile / virtual
claims processing
• Detect & avoid fraud, while supporting legitimate claims
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Compliance• Automated regulatory
assessments,
• Improved agility and risk management
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Observe and optimize
environment
Improve resilience and avoid downtime
Inefficient claims processing is costly and a major priority for insurers this year.
Processing claims make up 30% of operating costs on average.1
Inefficient processing leads to claims leakage, costing the insurance industry more than $30B/year.
Insurers experience leakage up to 25% vs. 3% industry benchmark.2
This is becoming the #1 priority for insurers in 2021.3
4
1: https://www.mckinsey.com/industries/financial-services/our-insights/from-transparency-to-insights-mckinseys-insurance-cost-benchmarking-20162: https://www.pwc.com.au/industry/insurance/assets/stopping-the-leaks-jan15.pdf3: https://go.forrester.com/blogs/europe-predictions-2021-financial-services/
AI can help process claims more efficiently and save insurers substantial amounts of money.
Reducing claims leakage can save insurers 5%--10% of their costs, translating to millions of dollars.4
AI can minimize potential claims leakage prospectively vs. diagnosing retrospectively (status quo).
Up to 70% of claims can be handled automatically.5
54: https://www.pwc.com.au/industry/insurance/assets/stopping-the-leaks-jan15.pdf5: https://www.digitalistmag.com/customer-experience/2019/04/10/digitally-fully-automated-claims-is-not-2030-dream-its-possible-today-06197774
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Claims TriageFirst Notice of Loss/Benefit
Assess complexity of incoming claims instantaneously for effective routing to surveillance teams, claims handlers, or automated resolution.
Claims LeakageAssignment
Automatically distribute claims to minimize claims leakage and efficiently optimize workloads across handlers.
Claims ProcessingManagement
Equip handlers with transparent and debiased AI predictions on the most likely outcomes and predicted settlement amounts.
Our Approach: Enhance claims management with speed, cost reduction and fairness
Speed Cost Reduction Fairness
©2021 IBM Corporation 24 July 20216
Input: Claim Records; Claims History; Complexity Assessment
Output: Complexity Rating
Inputs: Claim Records; Claim Adjuster Profiles
Output: Claim Adjuster Assignment
Inputs: Claim Record; Analytical Models
Output: Unbiased Claim Settlement
User Interface Business Use Case Technical Assets
• Repeatable, customizable,
modular
• Notebooks, AI Models,
business terms and more
• Demonstrable on Cloud
Pak for Data
• Address specific real-world
problems
• Show tangible business ROI
from applied data science
projects
• Interactive visualization of AI
output in business context
• Example customizable
business application
A development-ready AI project starter
customizable to your use case
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A packaged set of assets that address common business issues to kickstart your own implementation. End-to-end implementation, from data prep to deployment.
Data sources
Input your data using our sample catalog
Data schema
Define and map your data to the machine learning model
Watson Knowledge Catalog
Business terms and categories with predefined mappings
Machine Learning Developers
• Watson Studio• AutoAI• SPSS Modeler• Notebooks• CPLEX• Scripts
Machine LearningModels
Watson Studio – Deploy and manage the Machine Learning lifecycle with online or batch deployment
Model Monitoring• Explainability• Bias Mitigation• Model Drift
API Endpoints
Sample UI
Collect AnalyzeOrganize Infuse
Analytics Project
RETRAINING MODELS
✓ Increase speed to market
✓ Provide proven use cases for successful AI adoption
✓ Eliminate deliverable creation - tailor vs. create
✓ Deploy ready to scale assets
✓ Promote adherence to industry best practices
IBM Data Science and AI EliteDemo
9
Insurance Claims Leakage Accelerator
Industry Accelerator on IBM Cloud Pak for Data
IBM Data Science and AI EliteDemo
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Insurance Claims Processing Accelerator
Industry Accelerator on IBM Cloud Pak for Data
Data Science and AI/ March, 2019 / © 2019 IBM Corporation 13
1. Automating Claims Classifications and Regression
Tabular Claims data, containing both categorical and numerical features (e.g. age, dates, covered amounts)
2 Predictive Steps in the solution:
– Predicting likely outcome of claim (i.e. Settled or Rejected)
– Predicting possible claim leakage for assigned claim handlers
We let Watson Studio handle imputations and model choice as well as Feature engineering through Cognito1 and ADMM2
Cognito finds relevant features (e.g. through PCA or other simple transformations)
1: https://ieeexplore.ieee.org/abstract/document/7836821
2: https://arxiv.org/abs/1905.00424
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(c) IBM Corporation 2021
IBM Data Science and AI EliteDemo
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Watson Studio - AutoAI
Industry Accelerator on IBM Cloud Pak for Data
Data Science and AI/ March, 2019 / © 2019 IBM Corporation 16
2. Fairness in Claims Predictions
Certain features induce bias against a certain group of people (group fairness) or individuals (individual fairness)
Both human specialist and Watson Studio monitor this bias
For our example, bias against younger and older age-groups has been detected
Bias mitigation can be performed to assist the Claims Handler (by using for example Reject option classification1)
In addition to LIME2, we use Contrastive Explanations3 to explain the model’s decisions to help Claim Handler understand the decisions
1: https://ieeexplore.ieee.org/document/6413831
2: https://arxiv.org/abs/1602.04938
3: https://www.ibm.com/downloads/cas/0ZRZNR8E
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(c) IBM Corporation 2021
IBM Data Science and AI EliteDemo
17
Monitoring with Watson Studio
Industry Accelerator on IBM Cloud Pak for Data
Data Science and AI/ March, 2019 / © 2019 IBM Corporation
3. Optimization of scheduling
Based on predictions for the claims leakage, we solve a problem to find the optimal scheduling among claim handlers
Claim handler features and their workload are used as inputs to the claims leakage classifier
Optimal distribution between experience of claim handler, their case load and difficulty of claim case
CPLEX Optimizer allows for large-scale optimization across thousands of claims and handlers
4. Transparency is the Key
• It is not enough to model claims automatically but the whole data and modelling process needs to be documented
• AI Fact Sheets are important for any business using ML models
• Communicating what steps are used makes it easier for the consumer to understand how this could impact any decision
20(c) IBM Corporation 2021
Expected Outcome
If the claim agents could see the predicted
outcomes at point of claim notification, it would
lead to:
— Improved Customer Satisfaction and
Experience
— Greater consistency in claims routing.
— Reduced Time and Cost
Solution Components⎻ IBM Cloud Pak for Data⎻ IBM Watson Studio
“The Data Science Elite team really understood the persona of the claims handling agent, the outcomes from a customer perspective and the challenges we face from a data perspective and worked with the internal teams to design and build a proof of concept model that could produce tangible outcomes – great work”. Barry Hostead
Head of Data Management, MI and Analytics Insurance and Wealth
Lloyds Banking Group
IBM’s Data Science Elite team strives to improve customer experience by helping insurance claims handling agents make appropriate claim routing decisions by reducing time and cost
Industry: Banking and Financial MarketsGeography: Europe
Unique Challenge
— Siloed data across organization.
— First data-driven approach to predicting claims to assist agents.
— Claims-specific data is in both structured and unstructured formats.
Use Case
To embark on its digital transformation journey, Lloyds Banking Group wants to improve customer claims experience using a data-driven approach to support claim handling agents to make more informed routing decisions by leveraging their existing data
Conclusion
Inefficient claims processing is costly.
AI can reduce inefficiencies and costs but face ethical and scalability concerns.
We developed a solution that automates the creation of predictive and prescriptive models with explainable and fair results.
View our accelerators: https://community.ibm.com/accelerators
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