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

Practical Applications of Disruptive Technologies for the Insurance Industry

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

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

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

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)

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

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

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

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

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

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

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

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

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

What’s Next? Questions & Answers