Insurance Business Analytics Whitepaper

© Hexaware Technologies. All rights reserved. www.hexaware.com

Insurance Business AnalyticsActionable Intelligence Enabled

Whitepaper

version: 1.2 | Published on: 09/01/2012 | Alok Ranjan. Insurance CoE

WhitepaperInsurance Business Analytics

Table of Contents1 Introduction 03 1.1 Current scenario in Insurance Business Intelligence 032 Abstract 04 2.1 What Insurance Business Analytics can do? 043 Proposed Solution 06 3.1 Problem Statement 06 3.2 Introduction to Solution 06 3.3 Key offerings 07 3.4 Tangible ROI - Business Benefits 084 Future Direction 08 4.1 Long-Term Focus 08 4.2 Conclusion 08

2© Hexaware Technologies. All rights reserved. www.hexaware.com2© Hexaware Technologies. All rights reserved. www.hexaware.com

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Insurance Business Analytics

1 Introduction

1.1 Current scenario in Insurance Business Intelligence

With improvement in data warehousing techniques and advancement in storage capacity more and more data is stored in an enterprise warehouse. With improving data mining technology it is feasible to make knowledge discovery in a manner which was impossible earlier. New tool capabilities, new business models and new data sources are constantly emerging. They help in knowledge discovery and �nding actionable intelligence from internal data.

Insurance is a data intensive industry and insurance companies create huge data. Insurance companies realize that they have huge wealth of data which can be used to �nd answers to business related questions like

• Which distribution channel is best performing?• Where is an opportunity to upsell?• Is there a pattern in claim that is received?

Finding answers to questions like these was impossible and impractical earlier because of non-availability of reliable data and technology to support data mining. Through data mining and reporting technology it has now become possible to take a look at the enterprise data from di�erent dimension and derive actionable intelligence out of it.

Business Analytics is helps to align all the data within an organization to make sure data is accurate, timely, in context and available to all who need it. There is no dearth of such software and the choice depends on utility, price and performance. Once Insurance companies understand the power oftheir own data the requirement of knowledge discovery extends from reporting and dash-boarding to scoring and predicting.

Understanding

Integration

RAW DATA

Selection &Cleaning

Transformation

Data Mining

Industry Data - Various Source

Interpretation &Evaluation

DataWarehouse

Target Data

Pattern & Rules

How knowledge discovery works

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The HexSOA Test Model (HSTM)

2 Abstract

It goes without saying that the availability of correct data at right time is the key to taking timely decisions. On top of it if the business data is represented in most innovative manner, it helps understanding it quickly and drawing meaningful inference. Further if the business user is given option to drill down to the granular level, they may not ask for more.

Insurance Analytics solutions are designed in a way that it can draw required data from source systems (often disparate and even excel �les) on a regular basis. For achieving this one has to innovatively design the data model which can store all necessary data required to produce reports / dashboards. Once this data-model for insurance analytics is properly designed the key to its success lies in developing link with the data sources to get correct transaction data into the data warehouse at the de�ned interval. One has to design links in a way that it gets all relevant data from di�erent data sources at the same time. Other key requirement is designing the dash-boards so that it presents the data in most innovative way. The graphical representation of data allows end user to understand business performance at a glance for which otherwise they would have to shu�e through endless reports. Further these dashboards must allow end user to drill down to the minutest level and also download relevant data in desired format for further use. The interface designing is done by understanding the key performance indicators (KPI) which is most relevant to the insurance companies. This can be done either by asking the LOB managers to de�ne the KPIs which are most relevant to them or by bringing in industry knowledge and suggesting standard KPIs.

2.1 What Insurance Business Analytics can do?

Insurance Business analytics can hold the key to optimized performance. It provides actionable insights, trusted information which helps in taking informed decisions. By bringing together all relevant information in an organization, companies can answer fundamental questions such as;

• What has happened?• What is going right and what has going wrong?• Why is it happening? • Predict - what is likely to happen?

Some examples:

Premium Analytics

What is the overall position of Premium Income? What is the performance of di�erent distribution channels and how are they performing against their targets? It is important to know performance by geography drilling down to Zone, Region, Branch and so on.

Persistency Analysis

It is always pro�table to retain existing customer and the key indicator is persistency of business. Low persistency can be due to lapsation and it is important to understand the customer segment where lapsation is high. Our Insurance Analytics helps you to track persistency ratio on various dimensions and compare it not only with your company target but also with industry average.

Claims Analytics

Claims performance tracking is required to increase customer satisfaction and at the same time identify patterns. Analysis of TAT for various processes helps understand bottlenecks. Analysis of repudiated claims helps �nd possible pattern of fraud.

Business By ChannelTime Run:7/25/2011 2:38:56 PM

Agency Broker Corporate Agent

Direct

750

600

450

300

150

0

Cost

Claims Ratio AnalysisTime Run:7/25/2011 2:14:45 PM

Loss Ratio : 51.18

0

20

40 60

80

100

Expense Ratio : 26.78

0

20

40 60

80

100

Combined Ratio : 77.96

0

20

40 60

80

100

Lapse Ratio By ChannelTime Run:7/25/2011 2:28:47 PM

Agency

Direct Broker

Corporate Agent

0

8

16

24

32

40

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3 Proposed Solution

3.1 Problem Statement

In many Insurance Companies a divide exists between IT and a LOB managers need for knowledge and information. Storage systems help to accumulate transaction data every day and IT managers are required to create data dump for business users for their analysis and reporting. Data dump extracted from IT systems is passed on to business users which is used for analysis and reporting on periodic basis. The process is repetitive, time consuming and prone to human errors and having one view of business data is always a challenge.

Not that the insurance companies are not mining their own data but the way they do it is ine�cient and time consuming. Problem lies in the fact that most of these data is generated by disparate systems and there is a possibility of the data present in di�erent formats. Combining the data derived from disparate system to gain actionable knowledge through use of spread sheets cannot deliver information showing it from di�erent dimension. It is also possible that after doing an intensive exercise one may not derive information which allows view to minutest level and present them in most innovative manner.

3.2 Introduction to Solution

Insurance Business Analytics involves the following components.

• Data model – Data model supporting data marts with report-friendly three-tier hierarchy – summary, highly summarized, and KPIs• ETL – Extract, transform, and load technology for data movement, aggregation, scheduling, and error handling• Parameterized reporting frameworks – Provide a broad range of business segmentation, dependency analysis, trending, meeting needs

such as channel management, underwriting e�ciency, claims management, regulatory compliance, customer service, and distributor management

• Installation and implementation – Includes installation of the software, basic customization, documentation, and application training.

0

20

40 60

80

100

Lapse RatioTime Run:7/25/2011 2:14:45 PM

Persistency RatioTime Run:7/25/2011 2:14:45 PM

Target StatusTime Run:7/25/2011 2:14:45 PM

Total Business 60Renewed Business 42Industry standard- Lapse Ratio 15Lapse Ratio 30

Total Business 60Renewed Business 42Industry standard-Persistency Ratio 85Persistency Ratio 70

Target Premium(mn) 3.06Achieved Premium(mn) 1.26

Modify Modify

Modify

0

20

40 60

80

1000

20

40 60

80

100

CRM

Insurance Analytics

Enterprise DWH

ClaimsAnalytics

PremiumAnalytics

PolicyServicing

NewBusiness

Channel Analytics

Policy Admin System

ChannelManagement Claims Data

Claims Ratio AnalysisTime Run:7/25/2011 2:14:45 PM

Loss Ratio(%) 51.18Expense Ratio 26.78Combined Ratio(%) 77.96

Loss Ratio : 51.18

0

20

40 60

80

100

Expense Ratio : 26.78

0

20

40 60

80

100

Combined Ratio : 77.96

0

20

40 60

80

100

Ratio AnalysisTime Run:7/25/2011 2:14:45 PM

0

30

60

120

Laps

e ra

tio, P

ersi

sten

sy ra

tio

April August December February January July June March May November October September

Select View Channel Auto Trend

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3.3 Key offerings

The Insurance Analytics solution from Hexaware brings best of breed analytics which o�ers:

• Business user friendly reporting on traditional warehouse• Leverage In-Memory Analytic Tools for Quicker Access to data• Subject oriented Dimensional model; supports ad-hoc, self-service and dash-boarding capabilities• Independent of the BI platform• Industry standard and Customizable KPIs• Adapters to standard products and industry standard data models• Security level can be assigned for giving customized level of data viewing• URL based access on mobile device.

3.4 Tangible ROI - Business Benefits

• Going with the pre-de�ned data model helps faster implementation by 30% compared to e�ort required in creating speci�c data model• Hexaware Insurance Business Analytics e�ectively puts to end the dilemma of ‘buy v/s build’ decision. It is based on industry standard data model

and use best of breed reporting tools.

Key Analytics Areas• Premium Analytics• Channel Analytics• Policy Servicing Analytics• New Business Analytics• Claims Analytics

Insurance Processing – Subjects Data ModelA_EDUCATION

A_GENDER

A_CLAMANT

A_MARITAL_STATUS

A_PROFESSION

A_AGE_DRIVER

CHANNEL_D

PRODUCT_D

SEGMENT_D

ENGINE_CAPACITY

POLICY_TYPE_DA_VEHICLE_AGE

A_VEHICLE_COLOR

A_VEHICLE_MAKE

TIME_D

REPUDIATION_D

A_PROPERTY_STATE

A_PROPERTY_CLASS

A_TIMES_D

CLAIM_TYPE_D

CLAIM_CAUSE_D

POLICY_PERIOD

CLAIMS_TAT_F

TAT_PROCESS_D

AGE_D

CLAIMS_F

Premium Analytics

NewBusiness

PolicyServicing

ManagementDashboards

InsuranceBusiness

Intelligence

Channel /Marketing

Claims

1095 Cranbury South River Road, Suite 10, Jamesburg, NJ 08831. Main: 609-409-6950 | Fax: 609-409-6910

DisclaimerContents of this whitepaper are the exclusive property of Hexaware Technologies and may not be reproduced in any form without the prior written consent of Hexaware Technologies.

To learn more, visit http://www.hexaware.com

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4 Future Direction

4.1 Long-Term Focus

It is not always possible to do analysis of data in real time as the aggregation of data into a data warehouse has its own challenges. Mostly today the dashboards are available for past data, be it a day old or week depending upon the frequency on which the data is extracted into the DWH. However it appears that the business users wouldn't want to wait for information and BI users are beginning to demand real time or near real time, which is more in demand for data analysis relating to their business, particularly in front line operations. It is not too distant when insurance companies will demand real time business information. Monthly and even weekly analysis will not su�ce.

Other developing area is analytics o�ering on mobile devices. At present the BI dashboards and reports are being accessed using mobile devices either through URL or dedicated apps. As per industry estimates roughly 30 -35% of BI will happen through mobile devices by 2014. Moreover the trend may change from accessing reports using mobile devices to BI application developed for mobile devices.

4.2 Conclusion

Insurance is a data-rich industry; every insurance company has pile of data running into tera-bytes. But unfortunately, most of that data is either underutilized or unutilized.

There is growing awareness and demand to extract actionable knowledge from internal data which is key to gaining a competitive advantage by analyzing this data and getting a greater insight into their business. With help of Business Intelligence techniques Insurance �rms can unlock the intelligence contained in their operational applications - like policy administration, claims management, SPM and CRM solutions - through modern data mining technology.

Slice and dice of operational data can throw useful insights into monitoring the performance of producer, tracing process e�ciency of operations and decipher patterns form claims data. Not only this, a deep understanding of past data can be helpful to design new products and related marketing strategies, enabling the insurance �rm to transform their own data into a wealth of information and actionable intelligence.

Insurance Business Analytics Whitepaper

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Transcript of Insurance Business Analytics Whitepaper