uberization of insurance! - .net framework

Copyright © 2018 HCL Technologies | www.hcltech.com

Uberization of Insurance!D.I.C.E – Digital Insurance Claims Experience platform

2 Copyright © 2018 HCL Technologies Limited | www.hcltech.com

PreludeWhat's Next in Insurance?

The Banking, Finance & Insurance services industry is the widely and wildlyconquered sector where digitization and automation has been enabled to the max by IT.

But in recent times, with the advancement of technology and innovations, it is important forthe Insurance industry to explore, pace up, transform, innovate, re-think and live in thepresent.

The technology adoption and re-incarnation of business strategy/priorities to serve thecustomers will play a crucial role in the Insurance Company’s journey in making it stand talland strong in the crowd (as a differentiator).

Storm of Competition

Technology Shift

Regulatory Compliance

Customer Behavior

Key Drivers pushing the Insurance companies to embark on the Digital Journey

▪ As per FBI stats, the insurance industry in US consists of more than 7,000 companies that collect over $1 trillion in premiumsannually.

▪ Additionally, the total cost of insurance fraud (non-health insurance) is estimated to be more than $40 billion annually.▪ That means Insurance Fraud costs the average U.S. family between $400 and $700 annually in the form of increased

premiums. [1]▪ Insurance Fraud is no longer a victim-less crime, instead it has a cascading impact on all the stakeholders bearing the fraud-

loss▪ “Hence there is a great urge & demand to develop a system that can identify potential frauds with accuracy and arrest it at

the very initial stage for detailed scrutinization”

The M

arket Space

[1] Source: https://www.fbi.gov/stats-services/publications/insurance-fraud

https://www.fbi.gov/stats-services/publications/insurance-fraud


Uberization of Insurance!

D.I.C.E – Digital Insurance Claims Experience platform

- insurance claims risks & raffles to circumvent


Problem StatementTypical Challenges in an Auto Insurance Claims Process

This Photo by Unknown Author is licensed under CC BY-ND

Paper-forms that are manual & error-prone

Multiple Handoffs with limited transparency

Limited Input Channel Support

Data stored in pockets across various legacy systems

Reactive Detection of Fraudulent claims

Speed of Settlement is slow, impacting customer experience

Limited personalization or 1:1 engagement

Increased Operational Expenditure / Cost

https://www.quoteinspector.com/15-of-our-best-free-auto-insurance-images/

https://creativecommons.org/licenses/by-nd/3.0/


High-level SolutionPhase – 1 Implementation

Omniverse Sarah

Claimant using Web

/Mobile App / WhatsApp /

Voice Devices to converse

with Chatbot

Secured Access

Incl. Biometrics

Sending car accident information

and supporting documents /

proofs over Email

RPA Engine to extract information from Email and connect to Pega BPM Engine to create a

case and attach the necessary artefacts / supporting documents

Claim Database

Photo of Number plate

Claim Status Enquiry

Photo taken by

Personal Device

▪ Car Image taken during Policy Initiation

▪ Other Car Data retired for raising a Claim

Car Image taken after

Accident occurs

Up

load

to

Sara

h

En

ter

Cla

im #

AWS Machine Learning

Raise a accidental auto-insurance claims request via IVRS reaching out to a Call Centre Agent

Call Centre Agent

Email

Claim Status Query

Claim Status Information

Insurance Claims

Fraud Detection

Learning & Training the Model

Batch / Agent

Claim Request Submission & attach supporting images / documents

Number Plate image is OCRed to extract the

Vehicle Information Number

Omniverse

Cognitive

Platform

Custom

Vision AICar Image Comparison

for Accident Damage

Information Update Claims Info with

vehicle damage details


Technology Stack

Omniverse Cognitive Platform (NLP, Chatbot, Multilingual,

OCR, Virtual Assistant -Alexa/Google Assistant)

Robotic Process Automation (Automated email parsing &

processing of claims -leveraging UiPath)

AWS Machine Learning (Sagemaker) Library Integration for Fraud Detection / Prediction

Google Map APIs integration for Location & Street View

(Developer Version)

Pega Infinity (8.2.1) for Insurance Claim Case

Management Implementation & Batch Processing for Training

the Model

WhatsApp Integration for fetching claim status info

(leveraging Twilio platform)


Architecture at a Glance

[Initiate Claim] [Claims Review] [Sys Admin]

User Interface

Process & Workflow

[Claim]

[Admin]

[Delete AWS Model]

[Train/Retrain Model]

Agent / Batch Activity to

Generate CSV &

Train/Retrain Models

Pega RULES DB | Claims Database

Predictive

Models

AWS ML Connector

Shared Custom

Component

▪ Currently Pega does not have a connector for AWS ML (Sagemaker). This will helpcustomers planning to leverage AWS ML connectors for the machine learningcapabilities

▪ It may be available in the Q4 2019 by Pegasystems.▪ It is built leveraging custom libraries and config files to manage models in AWS

Sagemaker and creating/maintaining the same

Omniverse Sarah emailSelf-Service

Agents

Input Channel

Process Layer

Data Layer


▪ Leveraging Machine Learning & Predictive Analytics capabilities for Fraud Detection overcomes the traditional statistics perspective

▪ Enables Real-Time processing

▪ Process a variety of data to the algorithm without being judgmental around the relevance of the data elements.

▪ Leverage complex algorithms that iterate over large data sets and analyze the patterns in data

▪ The ML model works on a 3 stage cycle Train-Test-Predict.

▪ Optimizing the model by continuously adding data & experience makes predictions more accurate

▪ Self-Learning & Self-Healing capabilities help in automated fraud detection (unsupervised ML)

Train

TestPredict

Act

Fraud

Why Auto-Detection of Fraudulent Claims is important?Proble

m S

tate

ment

Solu

tion

Busi

ness

Benefi

ts

▪ Traditional fraud detection techniques are based on heuristic model & checklist driven fraud indicators

▪ Extensive manual intervention & dependency

▪ Inability to create a persona of the customer based on multiple dependent factors like relationship, segment, geo etc.

▪ No “Single-Go-To-Model” works every time for every customer

▪ Manual recalibration of model on a timely basis is challenging and also requires training plans to address such issues in future`

▪ The fraud detection processing involves multiple verification steps and is time-consuming

▪ Primarily Rules based – helpful in arresting obvious fraudulent scenarios but cannot comprehend hidden & implicit correlations in data

Manual Review is Costly

Time-Consuming for

Review & Training

Class Imbalance

Customer needs Quick

Resolution & TAT

▪ Machine learning can evaluate huge numbers of transactions in real time (in microseconds). It can be extended to adopt

advanced models such as neural networks for better accuracy/prediction & faster processing

▪ Machine Learning prediction quality improves with increased in data sets. Caution required in sanitizing the training data. As an

undetected fraud in Training Set can pose a risk for the system to behave adversely

▪ Unlike humans, machines don’t face problems like fatigue or boredom from routine/monotonous/mundane tasks.

▪ Helpful in detecting hidden and non-intuitive patterns for identifying fraudulent transactions

▪ Reduce cost for manual verification, review, and training the personnel

Reduce Cost

Reduction in TAT for

Fraud Detection

Improve Efficiency

Improve Customer

Experience

Reduce Manual Hand-offs

Improve Speed & Scale

Manual Error-Prone


Fraud Detection Use-Case Details (a functional view-point)

Fraudulent Claims Process (further scrutiny)

Claims ProcessingClaim is Genuine

Claim is Fraudulent

Claims

Fraud Inspector *

Insurance Company

Office Staff

Customer

▪ Existing knowledge base of fraudulent claims data is used

by the AI / ML models

▪ The entire process is self healing in nature (i.e the real data

is used on a periodic intervals to train the model)

▪ Based on the prior knowledge a prediction is made.

▪ Based on the accuracy of the model the process can be

fully automated (unsupervised machine learning)

▪ Involves Investigation process by Experts

▪ Need to involve other investigation parties

▪ Increase in cost and a time-consuming process.

▪ Existing Knowledge base of fraudulent claims data is not

used at all

▪ Primarily driven by Rules - cannot comprehend hidden &

implicit correlations in dataFra

udu

lent C

heck p

ow

ere

d b

y A

I / M

L

Tra

ditio

na

l wa

y o

f fraud

ule

nt c

heck

Experts Driven ApproachData Driven Approach

*The Claims Fraud Inspector has the authority to toggle the flag and make the Machine Learning Model

supervised or un-supervised based on the performance and accuracy of data predicted by the model

Toggle Switch


Business Benefits

Improve Productivity & faster time to market

Reduce Manual hand-offs & overheads with STPs

Reduce Operational Cost (OpEx)

Multi-lingual conversation and voice enabled assistants

Eliminate mundane & monotonous tasks with OCR & RPA

Personalized Customer Experience

Providing a human-face to your brand

Elevation of the Nature of Work

Improve Speed & Scale

Proactive detection of Fraudulent Claims with AI/ML techniques


Next Steps…

Google Vision APIs / ML

Enrich Application

Enhance RPA/Chatbot

Blockchain

Augmented Reality (AR)

Cosmos / Ux Kit

CRM Portal

Google Map View APIs

IoT Integration

• Parsing & Extracting data from DrivingLicense & Car Number Plate

• Inspecting and capturing the Damage spotsin the car (compare with old car image)

• Video analysis leveraging Data Science (MLlibraries) to assist in accident examination

• Street View and other Map Views todetect the accident site locationbased on map navigation

• Identify potential & likeliness of thesuch an incident at the site

• Contact Center Application leveraging PegaCPM for the Agent Portal

• Co-Browse Option• Intelligent Assistants• Next best Action & Recommendation• Integrate via Twilio or other platforms that

provide IVRS integration

• Enrich the customer experience & userexperience of the portal leveraging the latestUx kit provided by the Pega Infinity Platform

• Revamp the external UI where the Chatbot isembedded

• Identify & Predict Driving Behavior basedon data patterns captured via IoT devices

• Calculation Logic for Claim Amount basedon driving style

• Alerts & Notification Trace (sent before theincident if ignored by the driver) e.g.Weather Condition, Car Condition etc.

• RPA for parallel legacy system updatetypically done by an agent putting thecustomer on-hold (mimic legacy systems)

• Create a front-office & back-office RPAenabled process

• Enrich Chatbot Training & capability

• Leverage Augmented Reality to raise Claimsat the site location with a device and AR lens

• Identify potential spots that can be claimed• Can also be used by Site/Car Survey

personnel

• Enhance the Application & making it closerto reality:

o Data Properties | Sections | ClaimsFlow

▪ Data Sanity & Cleanup▪ Fine-tune Prediction algorithm▪ Revamp the end-user portal /agent portal

• Decentralized data storage that can befetched by various parties in the valuechain

• It will help in getting transparency andaccess to real-time status/data by anydiscrete system


Application Snapshots – for reference


Application Landing Page (end-user)


Omniverse – Digital Assistant “Sarah”


Claim Processing – Case Type in Pega


emailClaimBot: Processing the Claim Case creation based on email trigger

Claimant sends email with claim details,

license, car pic

UiPath Robot reads email, downloads pdf claim form and other

attached images

UiPath Robot parses the request details from the

PDF claim form

Prga creates a claims request and saves the

attachment in the request. Returns the ClaimID back to the

UiPath Robot

UiPath Robot notifies claimant about the claim

request details

UiPath Robot posts the claim data and images to

Pega by triggering a service


emailClaimBot: Snapshots

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RPA

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Form

| P

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Digital Assistant: Snapshots

WhatsApp Claim Status Enquiry

Cre

ate

Cla

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equ

est

Up

load

Do

cum

ents

Fetc

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Dat

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Span

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Google Assistant Alexa – Virtual Assistant


Pega Agent Desktop for raising Claims & Underwriting

Age

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$8.6 BILLION ENTERPRISE | 125,000 IDEAPRENEURS | 41 COUNTRIES

https://www.youtube.com/watch?v=JzfmzTcVUJg


Omniverse – An Intro!


Omniverse Core ConstructTechno Functional Platform Constituents


Omniverse Reference ArchitectureConceptual Building Blocks of the Platform


Omniverse Feature List

SMART BOT FeaturesMulti BOT Ecosystem

• Coexistence of BOT from multiple Technologies

• Live BOT Registration

• Many-to-Many relationship between BOTs and Brains

Multi-Channel Access• Custom Branded Web Chat

• Skype for Business & Skype

• Microsoft Teams

• SMS & Email

• Social like Facebook Messenger, Telegram, Twitter and Slack

• Digital Assistant like Cortana, Alexa and Google Assistant

Multi-Mode Communication• Textual Communication

• Voice Command

• Touch Commands

• Scribble Feed

• Gestural Input

• Behavioural Input for Proactive Action

Integrated Non-BOT Communication• Device Interfaces like MS Cortana, Google Home, Amazon Echo etc.

• Seamless Transfer to / from Live Agents

• Seamless Transition to / from Interactive Voice Responses

State Handling• Caching for Repetitive Data

• Hold User Session in Context for seamless Cross Channel transition

Cultural Diversity• Automated User Language Identification

• Response in User Language

• On Demand Service based Translation

• Predefined Resource based Translation

• Customizable BOT Personality

Power User driven BOT Management• BOT Parameters customizable by Power Users

• On Demand / On Schedule Activation / Deactivation of BOTs through

Registration Console

BOT to BOT Interaction• Cross Boundary BOT to BOT Communication

• Configuration based Interaction with third party BOTs

Non-Functional Boosters• Caching for Optimized Performance

• Secured Access through various Authentication Modes – LDAP

based, Multi Factor, Biometric integrated

• Itemized Auditing

Speech Capabilities• Speaker Recognition

• Speech Recognition

• Text to Speech and reverse

• Multilingual Speech

• Accent Management

SMART Brain FeaturesMulti Brain Ecosystem

• Coexistence of Brains from multiple Technologies

• Coexistence of Cross Functional Brains

• Massive Scale Out of Brain Ecosystem

Natural Language Expertise• Natural Language Processing, Query and Generation

• Strong Natural Language based Conversation

• Spell Check, Synonyms and Regular Expressions

• Content Moderation

Functional Diversity• Generic Query based Brains

• Personalized Query based Brains

• Personalized Transaction based Brains

• Proactive Brains working on Event or Data Correlation

• General Knowledge based Brains

• Casual Small Talk based Brains

Conversational Diversity• Direct Textual Communication

• Dialog or Prompts

• Hyperlink based Information

• Structured Feedback Processing

• Live Validation

• Embedded Content

• Text with Buttons

• Image Bubbles with Buttons

Controlled Learning

• Audit based identification of New / Unknown Utterances

• Auto loading of shortlisted Utterances to Brain(s)

• Automatic / Semi-Automatic Training

• Accuracy Booster Tools and Techniques

Personalization and Contextualization• Authorization on Use Cases

• Persona and User Property based Filter

• Contextual Hold

• Channel based Filter

• Trend based Filter

• User Sentiment driven Response

• User Vocal Tone Analysis

Power User-driven Brain Management• Consistent Brain Definition across base technology platform

• Live Brain Registration

• UI driven Use Case Management

• Multi-Level editing by Power Users

• Time driven Publishing Process

Reporting & Analytics• Integrated Analytics Engine for any Analytical Display

• Monitoring and Analytics with Power User Dashboard and User

Dashboard

• User Data Analytics

SMART Orchestration FeaturesMulti Recipe Ecosystem

• Cross Boundary Backend System Integration across Technologies

• Cross System Integration where cross-system trust is challenged

• Seamless connection with Brains

Power User-driven Orchestration Management• Recipe Registration

• Recipe Definition Management

• Publishing Ecosystem to control Orchestration availability

Multi-Level Orchestration Organization• Recipe Store holding all Recipes

• Cartridges as Building Blocks of each Recipe

• Cartridges comprises of

• Actions

• Connections

• Sequence

Standardized Integration Approach• All Integration through API Gateway only leading to better

management of Integration Definition

• Supporting any REST interface to be connected

• Keeping the core Integration Responsibility out of boundary

uberization of insurance! - .net framework

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