The document discusses insurance business analytics and how it can provide actionable insights from internal insurance company data. It proposes a solution using a dimensional data model, ETL processes, and parameterized reporting to analyze key areas like premiums, channels, policies, and claims. Implementing such a system using pre-defined models and best-of-breed tools could provide benefits like faster implementation and eliminating the need to build custom solutions. The solution aims to help insurance companies better leverage and understand their data.

1. Whitepaper
Insurance Business Analytics
Actionable Intelligence Enabled
version: 1.2 | Published on: 09/01/2012 | Alok Ranjan. Insurance CoE
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2. Whitepaper
Insurance Business Analytics
Table of Contents
1 Introduction 03
1.1 Current scenario in Insurance Business Intelligence 03
2 Abstract 04
2.1 What Insurance Business Analytics can do? 04
3 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 08
4 Future Direction 08
4.1 Long-Term Focus 08
4.2 Conclusion 08
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3. Whitepaper
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 finding 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 find 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 different
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.
How knowledge discovery works
Integration
Interpretation &
Evaluation
Industry Data - Various Source
Data Mining Pattern & Rules
Understanding
RAW DATA
Transformation
Target Data
Selection &
Cleaning
Data
Warehouse
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4. Whitepaper
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 files) 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 defined interval. One has to design links in a way that it gets all relevant data from different
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 shuffle 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 define 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: Business By Channel
Time Run:7/25/2011 2:38:56 PM
Premium Analytics 750
600
450
Cost
What is the overall position of Premium Income? What is the performance of 300
different distribution channels and how are they performing against their 150
targets? It is important to know performance by geography drilling down to 0
Agency Broker Corporate Direct
Zone, Region, Branch and so on. Agent
Persistency Analysis Lapse Ratio By Channel
Time Run:7/25/2011 2:28:47 PM
40
Agency
32
It is always profitable to retain existing customer and the key indicator is 24
16
persistency of business. Low persistency can be due to lapsation and it is 8
0
important to understand the customer segment where lapsation is
Direct Broker
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.
Corporate Agent
Claims Analytics Claims Ratio Analysis
Time Run:7/25/2011 2:14:45 PM
Claims performance tracking is required to increase customer satisfaction Loss Ratio : 51.18 Expense Ratio : 26.78 Combined Ratio : 77.96
and at the same time identify patterns. Analysis of TAT for various
processes helps understand bottlenecks. Analysis of repudiated claims 40 60 40 60 40 60
helps find possible pattern of fraud. 20 80 20 80 20 80
0 0 0 100
100 100
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5. Whitepaper
Insurance Business Analytics
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 inefficient 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 different formats. Combining the data derived
from disparate system to gain actionable knowledge through use of spread sheets cannot deliver information showing it from different 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 efficiency, claims management, regulatory compliance, customer service, and distributor
management
• Installation and implementation – Includes installation of the software, basic customization, documentation, and application training.
Claims Ratio Analysis Ratio Analysis
Time Run:7/25/2011 2:14:45 PM Time Run:7/25/2011 2:14:45 PM
Lapse Ratio Persistency Ratio Target Status
Time Run:7/25/2011 2:14:45 PM Time Run:7/25/2011 2:14:45 PM Time Run:7/25/2011 2:14:45 PM Loss Ratio : 51.18 Expense Ratio : 26.78 Combined Ratio : 77.96 Select View Channel Auto Trend
40 60 40 60 40 60
40 60 40 60 40 60
20 80 20 80 20 80
80 80 80
120
20 20 20
Lapse ratio, Persistensy ratio
0 0 0 100
100 100
0 100 0 100 0 100
60
30
Total Business 60 Total Business 60 Target Premium(mn) 3.06
Renewed Business 42 Renewed Business 42 Achieved Premium(mn) 1.26 Loss Ratio(%) 51.18
Industry standard- Lapse Ratio
Lapse Ratio
15
30
Industry standard-Persistency Ratio
Persistency Ratio
85
70
Modify Expense Ratio 26.78
0
Modify Modify
Combined Ratio(%) 77.96 April August December February January July June March May November October September
Channel New Policy Premium Claims
Analytics Business Servicing Analytics Analytics
Insurance Analytics
Enterprise DWH
Policy Admin Channel CRM Claims Data
System Management
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6. Whitepaper
Insurance Business Analytics
3.3 Key offerings
The Insurance Analytics solution from Hexaware brings best of breed analytics which offers:
• 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.
Insurance Processing – Subjects Data Model
A_EDUCATION A_MARITAL_STATUS
A_GENDER A_PROFESSION
A_PROPERTY_CLASS
A_CLAMANT
A_AGE_DRIVER
Channel / A_VEHICLE_MAKE
A_PROPERTY_STATE
Marketing A_VEHICLE_COLOR REPUDIATION_D
TIME_D
AGE_D CLAIM_TYPE_D
A_VEHICLE_AGE
Premium New POLICY_TYPE_D
CLAIM_CAUSE_D
Analytics Business
POLICY_PERIOD
ENGINE_CAPACITY
CLAIMS_F A_TIMES_D
SEGMENT_D
Insurance
Business CLAIMS_TAT_F
Intelligence CHANNEL_D
PRODUCT_D TAT_PROCESS_D
Management Policy
Dashboards Servicing Key Analytics Areas
• Premium Analytics
• Channel Analytics
• Policy Servicing Analytics
Claims
• New Business Analytics
• Claims Analytics
3.4 Tangible ROI - Business Benefits
• Going with the pre-defined data model helps faster implementation by 30% compared to effort required in creating specific data model
• Hexaware Insurance Business Analytics effectively 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.
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7. Whitepaper
Insurance Business Analytics
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 suffice.
Other developing area is analytics offering 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 firms 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 efficiency 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 firm to transform their own data into a wealth of information and actionable intelligence.
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