1. Fall Midwest IASA Conference
Council Bluffs, Iowa
September 12-13, 2019

2. Practical Applications of Disruptive Technologies for the Insurance Industry

3. Objectives Clarify the A.I. “Terminology”
Practical Applications: Today & Tomorrow
Challenges & Potential Solutions

4. A.I. - Defining the Terminology
Degree of sophistication & ‘intelligence’:
Deep Learning
Natural Language Processing (NLP)
Predictive Analytics
Machine Learning (ML)
Robotic Process Automation (RPA)

5. A.I. - Defining the Terminology
Degree of sophistication & “intelligence”:
Programmable algorithms or rules engines
Used to automate consistent, repetitive tasks
Robotic Process Automation (RPA)

6. A.I. - Defining the Terminology
Degree of sophistication & “intelligence”:
Supervised models
Constrained inputs & parameters
Trained via historical data
Make predictions or decisions based on training + new data received
Machine Learning (ML)

7. A.I. - Defining the Terminology
Degree of sophistication & “intelligence”:
Predictive Analytics
Make predictions based on historical inputs and learned behavior
Require large amounts of data to predict with high accuracy/confidence
Results from training machine or deep learning models
Incorporate user feedback & input to improve confidence
Quality of data input critical to yield reliable, accurate predictions

8. A.I. - Defining the Terminology
Degree of sophistication & “intelligence”:
Natural Language Processing (NLP)
Parses unstructured data
Understand context
Extract key data from text

9. A.I. - Defining the Terminology
Degree of sophistication & “intelligence”:
Deep Learning
Population-Based Training
One model learns from the “fittest” other models
Accelerates selection of ML algos & parameters
Advanced form of Machine Learning
Leverages multi-layered neural networks
Don’t require human programmers to teach them
Can be unsupervised to discover new patterns
Unconstrained inputs & parameters

10. Current Front-Office Applications in Insurance
ML & Predictive Analytics
Loss forecasting
Actuarial modeling
Fraud detection

11. Current Front-Office Applications in Insurance
RPA & NLP
Application Processing
Claims Management
Custom Service

12. Middle & Back Office Applications in Insurance
Estimated Efficiency Gains
Defines, implements & continuously learns matching & reconciliation rules
80-90%
Trade Processing
Trade Capture
Manual Trade Entry
Matching
Affirmation & Confirmation
Cancels & Corrections
Security Master & Data Validation
Price Checks
Securities Attributes
New Securities Setups
Tolerance Checks
Investment Accounting
Asset Valuations
Accruals & Cash Flows
GL Postings
Transfers & Impairments
Reconciliations
Cash & Positions
Sweeps
OMS & Custodians
Failed Trades
Break Repair ML PA
Principal & Interest
Price
Quantity
40-50%
Predict & populate select data fields based on historical transactions & holdings data
RPA
20-30%
RPA
RPA
Securities Event Processing
Corporate Actions
Loans, LPs and Private Equity Events
Factors, Prices, Ratings & Rate Updates
Collateral Management
Inventory & Pledging
Wire Transfers
Margin Calcs
Securities Lending
Hedging & Linking
Financial Reporting
Corporate GL
Data Warehouse(s)
Financial Analysis
Investor & Reg Reporting
SOC1 & Monthly Audit Checklists
Reads & parses data from agent bank docs
Sends user repairs to NLP engine to improve accuracy
40-50%
Yield walks
Proofs
Income swings
RPA
20-30%
NLP
Innovative Technology Key
Robotic Process Automation
Machine Learning
Natural Language Processing
Predictive Analytics PA
RPA ML
NLP PA

13. Middle & Back Office Applications in Insurance
Estimated Efficiency Gains
Future State – With Robotic Process Automation
Current State - Sample Operational Workflow
Future State – Estimated Efficiency Gains
Future State – With Natural Language Processing
Future State – With Predictive Analytics
Future State – With Machine Learning
Trade Processing
Trade Capture
Manual Trade Entry
Matching
Affirmation & Confirmation
Cancels & Corrections
Security Master & Data Validation
Price Checks
Securities Attributes
New Securities Setups
Tolerance Checks
Reconciliations
Custodians
OMSs & Cash Mgmt Systems
Data Warehouses
Corporate GLs
Break Repair ML PA
Matches custodian & OMS files with cash & positions to identify breaks and continuously learn as formats change
80-90%
Predict & populate select data fields based on historical transactions & holdings data
RPA
20-30%
RPA
Principal & Interest
Price
Quantity
30-40%
Investment Accounting
Asset Valuations
Accruals & Cash Flows
Journal Entries
Transfers & Impairments ML
Learns user behavior and recognizes activity patterns to suggest STP processing improvements
30-40%
Yield walks
Proofs
Income swings
Read & parse data from agent bank docs
User repair & feedback to NLP engine
Financial Reporting
Corporate GL
Data Warehouse(s)
Financial Analysis
Investor & Reg Reporting
SOC1 & Monthly Audit Checklists
20-30%
50-60%
Securities Event Processing
Corporate Actions
Loans, LPs and Private Equity Events
Factors, Prices, Ratings & Rate Updates
Collateral Management
Inventory & Pledging
Wire Transfers
Margin Calcs
Securities Lending
Hedging & Linking
RPA
NLP PA
Innovative Technology Key
Robotic Process Automation
Machine Learning
Natural Language Processing
Predictive Analytics ML
RPA
NLP PA

14. Emerging Applications in Insurance
Middle & Back-Office:
Deep Learning
Natural Language Processing ??
Property management data
Partnership agreements
Loan covenants
Predictive Analytics
Intelligent navigation
Cash flow forecasting
Additional financial insights
Machine Learning
Cash sweeps
Data integration / normalization
Robotic Process Automation
Securities event processing
Collateral management
Updates to General Ledger

15. Key Challenges and Solutions
Legacy architecture
Lack of tech/business expertise
“Black box” concerns
Limited data sets to adequately train models
Solutions:
Leverage the cloud
Partner with experts
Demystify with transparency
Scale through outsourcers

16. What’s Next?
Questions & Answers