Presentation on Claims Analytics

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AAUM Confidential
Claim Analytics

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Corporate profile
Founded by IIT Madras alumnus having extensive global business experience with Fortune 100
companies in United States and India having three lines of business
Prof Prakash Sai
Dr. Prakash Sai is professor at the Department
of Management Studies, Indian Institute of
Technology Madras. He has wealth of
international consulting experience in Strategy
Formulation
Puneet Gupta
Puneet spearheads the IFMR Mezzanine
Finance (Mezz Co.), is strengthening the
delivery of financial services to rural households
and urban poor by making investments in local
financial institutions.
Padma Shri Dr. Ashok Jhunjhunwala
Dr. Ashok Jhunjhunwala is Professor at the
Department of Electrical Engineering, Indian
Institute of Technology Madras India. He holds a
B.Tech degree from IIT, Kanpur, and M.S. and
Ph.D degrees from the University of Maine, USA.
Analytics
• Appropriate statistical models
through which clients can measure
and grow their business.
Competitive Intelligence
• Actionable insights to clients for
their business excellence
Livelihood
•Services ranging from promotion of
livelihoods, implementation services,
livelihood & feasibility studies.
 Key Focus Areas in Advanced analytics and Predictive analytics
 Product – geniSIGHTS (Analytics/BI), Ordo-ab-Chao (Social Media)
 More than 25 consulting assignments for Businesses & Govt orgs
 Partnership – Actuate, IIT Madras, TIE and 3 strategic partnerships
 Dedicated corporate office at IIT Madras Research park since 2009
Aaum’s office, IIT Madras Research Park

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Competencies in
Advanced analytics
Build appropriate statistical models through
which clients can measure and grow their
business.
Expertise in
• Digital Media
• Finance/Insurance
• Retail
• Entertainment
• Human Capital
• Government organizations
• Research & training
Competitive
assessment
Competitive intelligence
Provide actionable insights to clients for
their business excellence.
Expertise in
•Business Entry
•Business Expansion
•Market research
Livelihood
Perform livelihood services ranging from
promotion of livelihoods, implementation
services, livelihood and feasibility studies.
Expertise in
•Government organizations
•Non Government
organizations
•Corporate with livelihood
focus
•Research

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Powering Business Intelligence with Predictive Analytics

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is conceptualized by a simpler workflow based on our discussions with
the client
Data warehouse
Data warehouse
(Analytical)
ETL Tools
Dashboards/
Reporting
Claims Analytics
Client Aaum
Provided to
Aaum as Flat
files/CSV files

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Design overview
Client
Data warehouse
Analytics
Data warehouse
Engine
Web Control Panel
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Aaum will design
1. The web control panel – To provide mechanisms to start, stop,
change some of the control variables, etc.
2. R engine – that will perform analytics.
3. Devise mechanisms to pull out new/updated transactions from the
Analytics Datawarehouse
4. Devise mechanisms to push the analytical insights at predetermined
time period – Claims forecasting, Date of realization, probability of
realization, accuracy
User Input/Update
Claims dashboard

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Healthcare Claims Intelligence Solution - Analytics module - High level
requirements - Management
Interface to find the current execution status  Show details button – will print the details of executon in the
control panel.
The call should be an asynchornous call and once
the job is completed, it has to do a call back or there
should be way to find if the job is completed
 The results willbe pushed into analytics DB.
To resume the current execution of the job if it has
been stopped in the middle.
 The resume button in the web control panel. (Will look into
the log file & resume from the interruption)
Integration interfaces to be exposed for stopping the
analytics engine  The Stop button in the web control panel
Integration interfaces to be exposed for starting the
analytics engine
 The Start button in the web control panel
Ability to store the audit details such as start time
(date) & end time (date) of each execution, accuracy
levels, no of records processed etc.
 Log file will be generated.
To know the accuracy level of the current run  The results will be pushed into analytics DB
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Healthcare Claims Intelligence Solution - Analytics module - High level
requirements - Configuration
To notify (alert) if accuracy level reaches threshold  The results willbe pushed into analytics DB.
To set the configuration parameters (to be
elaborated further during the design phase)
 Web control panel
To set the analytics DB  R Data Import/Export functionality
To set the source DB  Client‘s responsibility
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Healthcare Claims Intelligence Solution - Analytics module - High level
requirements - Functional
To handle scenarios where it doesn’t have historical
data for prediction (scenarios like new Payer or new
treatment or new type of claim etc) - configurable
 Based on the discussion with the Business
To have a flag to denote if there is any change in the
above three compared to the previous values (so that
only those records can be updated from Analytics
DB to Datamart)
 The results willbe pushed into analytics DB.
Expected date of realization  The results will be pushed into analytics DB
To calculate the Amount that will be realized  The results will be pushed into analytics DB
To calculate the expected Probability of realization  The results will be pushed into analytics DB
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Healthcare Claims Intelligence Solution - Analytics module - High level
requirements – Non Functional
Hardware requirements
Memory – 12 -16 GB memory
Hard Disk Space – 1 TB {Based on data}
Database Recommended - MySQL
Window time (processing time)  TBD
Packaging along with the solution  R, R packages, SQL scripts, R scripts, Apache,
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powered by structured methodology in developing claims analytics
framework based on the understanding of the objective, modeling
expertise, rules formulation & statistical tools selection
Development Sample
Statistical
Modeling
Business rules
formulation
Statistical
tools
Claims System
New claims record
LDA, Logistic, Neural networks,
CART, k-nn, random forests,
textmining
Automation of the credit
scores & strategies
Modelevaluationandfinetuningatperiodicintervals
Strategic rules to provide
meaningful insights
Claims decisions
Information from
external sources
Existing Customer data
Information from
external sources
Revenue forecasting,
Claims bucketing
Date of realization
Model accuracy

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with provisions to utilize analytical insights using web services and/or
open standards
Executing R scripts by webservices
 Model development in R
 Aaum will create create a web
application that will run R scripts .
 Client can integrate this application with
the dashboard/reporting interface.
PMML
 Model development in R
 Model export in PMML
 IBM SPSS to interpret the PMML
 Not sure – how well SPSS supports
advanced PMML.
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that would address the current challenges in Claims Processing
• Increasing claims fraud. Fraud is on the rise, compounded by the economic
slowdown, and fraudulent activity is typically not discovered until after a claim is
paid.
• Inaccurate loss reserving. Loss reserves that are too low or too high can result in
inadequate pricing that puts the company at a competitive disadvantage.
• Unstructured data. Up to 75 percent of claims data is unstructured, resulting in
time-consuming, manual investigations.
• Limited resources. A shortage of expert adjusters and cost rationalization issues
have resulted in overworked and understaffed claims departments.
• Rising legal costs. Claims settlement costs are typically doubled when an attorney
is involved, and these costs are a growing threat to profitability.
• Inefficient claims prioritization. It’s difficult to determine how to effectively
prioritize claims so that the ones requiring immediate attention can get it.

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with the insightful insights from our expertise
 Categorize the claims by its risk based on the analysis of historical data.
– Enable hospitals to optimize their resources, efforts effectively for claims realization
 Predict the cashflowsSeverity of the claim, likely amount of time before settlement.
from the pending claims based on claims scoring
– Predictive scoring algorithms written to help organizations to make the best choices to
optimize their use of available time and resources.
– Effective funds utilization, Loss reserving , Fall back mechanisms, etc
• Quick decisions!
 Improve Predictive Accuracy by Segmenting
– Segmentation done using variables associated with risk factors, profits or behaviors.
– Segments based on these types of variables often provide sharp contrasts, which can
be interpreted more easily. can more accurately predict the likelihood of a claim and
the amount of the claim.
 and finally hospitals can combine the disciplines of payment, activity, fraud and
recovery optimization into one cohesive framework.

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“Organizations are competing on
analytics not just because they can-
but because they should…”
Thank You