CLEA is a cognitive assistance and mentoring application using machine learning and artificial intelligence to support human claims adjusters in real-time. CLEA also supports personal and individualized training based on actual adjustor performance.

1. 1
Claims Mastery
An application of cognitive learning and
‘mentoring’ technologies to improve the claims
process
Edward H. Vandenberg
September, 2019

2. 2
Claims Learning Excellence Application - CLEA
© 2019 Teradata
CLEA is a cognitive assistance or ‘mentoring’ application for insurance claims adjustors
• Improve Claims Outcomes
• Improve Customer Satisfaction
• Take out wasted expense

3. 3
Loss Adjustment Expenses are Eating Profit
© 2019 Teradata
What’s Happening: Insurers are challenged to maintain
profitability because of having to deal with low interest
rates, increasing competition and market overcapacity.
Technology: A Starting Point . Best’s Review August, 2019
AM Best expects personal lines rate increases in 2019 to
be higher than for commercial lines, reflecting increasing
severity trends, even as companies continue to focus on
expense efficiencies. In personal auto, rising medical
expenses and costlier repairs of increasingly sophisticated
vehicles are driving higher rates.
US Property/Casualty Financial Results Improve
Best’s Market Segment Report (Excerpt): 2019 Review & Preview: US Property/Casualty (Feb.
26, 2019)
It is very hard to scale people to the job needed in claims and maintain quality and performance

4. 4
Skills and Experience – a key challenge
© 2019 Teradata
A Pew Research Center study found that by
the end of 2020 there will be 400,000 unfilled
job positions in the U.S. insurance industry.
Within the next three years, 25% of the
industry's professionals will retire. Yet, the
study also found that only 4% of millennials
expressed interest in working in the insurance
industry.
Key people are retiring and new joiners (if they can be recruited) lack the experience to maintain quality
and performance

5. 5
Labor Costs and Productivity are Holding Back Performance
© 2019 Teradata
The business model is being challenged to keep up with the complexity and volume of the business

6. 6
Real-time Analytics and AI – Bridging the Gap
© 2019 Teradata
Machine Learning and Artificial Intelligence applications will bridge the gap between talent and tasks in
routine cognitive tasks for claims settlement
• Deep Learning
• Cognitive Assistants
• Gamification
• Feedback and personalized
training

7. 7
What and Why?
© 2019 Teradata
1. Reduce labor costs
2. Improve NPS
3. Shorten the claims lifecycle
4. Make adjusters more efficient and more
engaged
5. ‘De-risk’ the loss of experience experts in
your claims practice
Implement a real-time cognitive mentoring system that would gradually become a stand alone
automated claims assistant for self-service claims adjusting

8. 8
Teradata Vantage – the technical foundation for CLEA
© 2019 Teradata
Powerful access to leverage all of the data, all of the time.
So you can deliver analytics that matter.
A single platform for descriptive, predictive, prescriptive
analytics, machine learning functions, and visualization tools.
Gives customers powerful, flexible Pervasive Data
Intelligence across the organization.
Rated one of the worlds leading vendors for Data Management Solutions for Analytics, Gartner Magic
Quadrant, February 2018

9. 9
Discussion
© 2019 Teradata
1. Evaluate/collaborate on a detailed solution and use
cases
2. Joint business case development
3. Joint application design/scope and release
4. POC Outline
Teradata has a detailed Solution Design document and Use Case Inventory for CLEA, created and
validated by claims experts. This is the basis upon which we would engage in a joint effort to design a
custom application.

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Review of Solution and Use Cases
Appendix

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Solution Document
© 2019 Teradata
Executive summary
This initiative will create an application for cognitive assistance or ‘mentoring’ for insurance claims
adjustors to improve their handling practices that contribute to claim outcomes and the overall
performance of the claims function.
The Claims Learning Excellence Application (CLEA) will ultimately create a more
competitive company by reducing loss adjustment expenses and by improving the Net Promotor
Scores of our customers, leading to higher retention. The initial system will apply to high
transaction claims in complex auto coverages such as bodily injury, and liability claims for personal
lines auto.
This concept is supported in technical and academic literature. Readers can review especially the following article: “Mentoring: A Leverage Point for
Intelligent Systems?” Robert R. Hoffman, Paul Ward. IEEE Intelligent Systems, Vol 30, no. 5, 2015
https://ieeexplore.ieee.org/document/7243217

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Pro forma Scope (data)
© 2019 Teradata
Data In-Scope
First Notice of Loss (FNOL)
1. Transaction System new claim (first party and third party)
2. Transaction system – prior claims (including family members or household)
3. Policy system – basic coverage and premium data
4. IRV Voice (claimant and claims adjustor)
External Data for Analysis
1. Prior Claim Lookup – prior carriers
2. Demographic Classes applicable
3. Reference information for geographically relevant data (e.g. local medical and repair cost)
4. Social Media (claimants and related parties
Note that the initial facts of the claim are minimal at the time the segmentation takes place. Therefore, it is critical
to broadly assess relevant third-party data to be appended to the claim record to successfully find hidden claim
attributes.

13. 13
Use Case Inventory – Primary Use Case
© 2019 Teradata
UC1.0 Claim Typing
The component that 'types' an
individual claim as comparable
to a body of historic claims.
Strong typing ensures that the Case Advice is as
accurate as possible for a homogeneous group of claims
(meaning all the facts known and deducted with high
confidence)
UC 2.0
Case Advisor
(CLEA)
The components viewed by
the Claim Processor which
informs case-based decisions
and inventory-based decisions
This is the case-based, computer-generated statements
or indicators for handling an individual claim, focusing on
complex cognitive tasks at hand. Indicators include visual
models; the claims processor and the Case Advisor are
engaged in a game of competition for the best actual
outcome. Included in the competition is a decision made
by a 'crowd' of claim processers.
The inventory consists of 11 Use Cases

14. 14
Thank you.
©2018 Teradata
Thank you.
©2018 Teradata