Smarter Analytics Leadership Summit - IBM...Transactional Credit Card ... Enormous amounts of data...

Smarter Analytics Leadership Summit Content Review

Agenda

Fraud Point of View

IBM Claims Fraud Solution Overview

Infinity Insurance: Combating Fraud with IBM Claims Fraud Solution

Building the Business Case

© 2013 IBM Corporation2

Cross Industry Problem – focused Industries include

Anti Money Laundering• Sanctions screening• KYC scoring• Suspicious Activity

Online Fraud

Transactional

Credit Card

Internal/Employee

Loan or Application

First Party Fraud

Tax/Revenue • IRS, • SSA

Healthcare • Medicaid• Medicare• DoD• others

Food & Nutrition Programs• Food Stamps• WIC

Compliance and Audit• Departments• Agencies

Social Services

Claims Fraud • Cash for Crash• Staged accidents / thefts• Exaggerated claims• Provider fraud

Policy fraud• Fake policies / documents• Rate jumping• Online fraud

Internal / Employee fraud• Fake policies / documents• Pocketing premiums• Workers Compensation

Provider/Vendor Fraud

Pharmaceutical Fraud

Physician / Employee Fraud

Banking Insurance

Public Sector Health Care


We integrate technology, people, and experience

Consulting Expertise Intellectual Capital (Asset)

Software Assets and Capabilities

Thousands of cross industry risk indicators

Many years of experience and over a hundred global fraud, waste and abuse

implementations

Best-in-breed software capabilities enable organizationsto harness the IP and Expertise in a complete Fraud, Waste

and Abuse solution


What our insurance clients telling us about fraud…

Fraud is pervasive, growing and has become a board room issue

Fraudsters are much more sophisticated, technology enabled and transnational

Fraudulent activity continues to rise, methods more sophisticated

Organized crime rings are more prevalent

Continued and growing concern about e-commerce attacks

Insiders (employees) are a prominent threat

Enormous amounts of data to uncover fraudulent activity is a daunting task (Structured & Unstructured)

There is simply not enough resources to attack the problems


IBM’s Claim Fraud Point of View

For Smart Insurers, claim fraud is not treated as a point solution or as a step in the process. It is more than a “score”. It starts before Intake. It is seen as preventable,

predictable, provable, and is managed pervasively across the claim life cycle


Attacking fraud is one aspect of transforming to Smart Claims

Business Analytics & Optimization to Improve Claim OutcomesBusiness Analytics & Optimization to Improve Claim Outcomes

Smart Claims initiatives use Business Analytics & Optimization go beyond automation and core transformation to optimize outcomes across all aspects

of the claim life cycle


One potential source for improving Combined Ratios is reducing Claim FraudClaim Fraud may account for as much as 10% of a carriers Incurred loss expense…which is especially challenging in today’s market with Combined Ratios rising well above 100

Fraud is a hidden cost – not fully known

• Historically accepted as a cost of doing business

• Very expensive to find and reduce as was very labor intensive

Market conditions are pressuring the bottom line

• Soft market pressuring revenue and profit

• Low interest rate limiting investment returns

• Operational costs already cut to the bone

Crime rings are increasingly turning to fraud

• Organized crime is seeing a low risk, high reward potential

• Banking is more difficult due to AML, so seeing a shift to Insurance

• Lower risk…courts are clogged with violent crimes

Economic downturn historically leads to greater fraud and abuse

• Opportunistic fraud rises during bad economic eras

• Advances in analytics make finding fraud possible & economical

• Higher volume and velocity of data available to identify fraud

• Cheaper, more sophisticated, and faster running models

Carriers are increasing their investments in finding and preventing fraud

FraudDetection

$15+ Billion

savings opportunity

1 to 3%

10%

0%

All Claims

Fraud

100%

Industry estimates suggest 10% of claims are fraudulent, but the actual figures are likely higher.

Historic best practice only find 1-3% of fraudulent claims

Technology is poised to change this and allow the economic

recovery of more fraud

Industry estimates suggest 10% of claims are fraudulent, but the actual figures are likely higher.

Historic best practice only find 1-3% of fraudulent claims

Technology is poised to change this and allow the economic

recovery of more fraud


Carriers find the obvious through

normal adjudication process. Room for improvement to find less obvious using

better tools.

Common focus for most P&C (and some Life) carriers. Room for improvement as this is being fought

with “traditional” tools

Carriers are expanding their focus to detect and prevent more types of fraud

Fraud originates from various sources using creative schemes. The combination of new technology and challenging business conditions makes uncovering Opportunistic fraud economically viable

Opportunistic

Organized

Mature for high volume or regulated

Not as strong for low volume/non-

Regulated providers

Currently viewed as too costly to uncover

anything but the obvious. Viewed as a

“Cost of Doing Business”


Carriers can be much smarter when it comes to finding and stopping fraud

Lack of Insight

Inability to Predict

Inefficient Access

Variety

VolumeVelocity

Predict and Act(Predict at Intake)

Real-time, Fact-driven(Data Driven)

Smart Claims Approach

Sense and respond(After the Act)

Instinct and intuition(Adjuster Driven)

Traditional Approach

Automated(Claims Admin)

Skilled Adjusters(Instinct)

Back office(SIU)

Optimized(Case Management)

Everyone(Insight)

Point of Impact(Agents, Adjusters, Investigators)

…Results in rapid, informed and confident actions

Today’s insurance companies must transform to reduce costs



IBM Claim Fraud Appliance


Rules Library

Uniquely identifies claim entities and relationships

Manage, Automate, and Collaborate Cases

Predict Suspicious and Anomalous Behavior

Identity Resolution / Social Network Analysis

Resolves identities across claims

Identifies relationships across entities

Business Rules

Industry specific rules• Ex: More patients per day than is possible

Business expertise rules

Predictive Models and Anomaly Detection

Combines structured and unstructured big data

Identifies anomalous behavior – claim, provider, employee, etc.

Case Management

Centralized referral management

Collaboration for improved efficiencies and results

Integrated text mining of social media and other big data forms

Closed Loop Analysis for Fraud Metrics

Fraud Insights

Monitor fraud metrics ensures performance

Provides insights to front-line managers and executives

Robust Approach To Fraud Prevention, Detection and Investigation


13

EAS Entity #9453

Attribute Value Source

Name Marc R Smith A-70001Name Randal M Smith B-9103Name Mark Randy Smith C-6251Address 123 Main St A-70001Address 456 First St C-6251Phone (713) 730 5769 A-70001Phone (713) 731 5577 B-9103Phone (713) 731 5577 C-6251Tax ID 537-27-6402 A-70001License 0001133107 A-70001License 1133107 C-6251DOB 06/17/1934 B-9103

EAS Entity #9453


Name Marc R Smith A-70001Name Mark Randy Smith C-6251Address 123 Main St A-70001Address 456 First St C-6251Phone (713) 730 5769 A-70001Phone (713) 731 5577 C-6251Tax ID 537-27-6402 A-70001License 0001133107 A-70001License 1133107 C-6251

EAS Entity #9452


Name Randal M Smith B-9103DOB 06/17/1934 B-9103Phone (713) 731 5577 B-9103

Observations

Identity Resolution – “Who is who?”

Record #70001Marc R Smith123 Main St

(713) 730 5769537-27-6402

DL: 0001133107

Record #9103Randal M Smith

DOB: 06/17/1934(713) 731 5577

Record #6251Mark Randy Smith

456 First Street(713) 731 5577

DL:1133107

EAS Entity #9451


Name Marc R Smith A-70001Address 123 Main St A-70001Phone (713) 730 5769 A-70001Tax ID 537-27-6402 A-70001License 0001133107 A-70001

OCC

Gang

Arrest

Interactions Entity Context


Social Network/Link Analysis

Visual and mathematical analyses of complex human and computer networks

Discover sources and distribution of power: Who knows whom; who does business with whom; and who wields greatest power

See how communications flow in networks and sub-networks

Monitor patterns between communications nodes, performance and key goals

Combines data extraction, manipulation, analysis and visualization


Decision Management Rules Support

Rules Library and Execution

Incorporates domain and business expertise to identify suspicious claims

Configure business rules easily

• No programming

• No IT required

Rule to value based assignment

• Different rules have different risk

Create and manage thresholds

Graphical, thin client interface

Simulate and test scenarios


Predictive Analytics and Anomaly Detection Uncover Fraud Patterns

Combines structured claims, policy and billing data with unstructured data like claim notes

Identifies behavior that is uncharacteristic of similar claims, providers, attorneys, etc


Adjuster and Investigator notes can be used in fraud detection models

Combining this vital data with customer, policy, and claim data provides the most robust application for fraud detection


Detection Engine: Optimize Fraud Decisions for Better Results

Flexible and adaptive environment for fraud identification

Combines Rules, Predictive Analytics, and Social Network Analysis

Real-time claims monitoring

Business led application

• Easily modify rules and decision outcomes

• Little reliance upon IT

• Less reliance upon Quant department

• Simulation and What/If Analysis

Sophisticated visualization for identifying non-obvious patterns and relationships


Case Application Design

Case Lifecycle Management

Case Infrastructure Case ContextCase Runtime

Framework

CaseAnalyticsCase Templates Case

Tasks

Workflow Monitoring & AnalyticsCollaborationRulesContent Social

SoftwareEvents

360o View of Case

Advanced Case Management

people o process o information

Leverages Web 2.0 standard with user configurable widgets, Event processing, built in text analytics, instant messaging

and collaboration tools

Case Management


Claims Management Dashboards Provide Immediate Insights


The IBM Claim Fraud Appliance was invoked in real time and it

was determined to be a high risk

The IBM Claim Fraud Appliance was invoked in real time and it

was determined to be a high risk

The Fraud Rules presented a low probability of fraud

The Fraud Rules presented a low probability of fraud

However, the analytical model – using claim description notes – indicated a

high probability of fraud

However, the analytical model – using claim description notes – indicated a

high probability of fraud


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