Aaum's - Presentation on Analytics for Matrimony Services

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AAUM Confidential
Analytics for
Matrimonial

2. - 2 -
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

3. - 3 -
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

4. - 4 -
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
Competitive Advantage –Advanced analytics, Predictive analytics
Executed more than 25 consulting assignments for Businesses and Govt organizations
Team of 20 members
Dedicated corporate office at IIT Madras Research park since 2009
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.

5. - 5 -
Competencies in
Advanced analytics
Build appropriate statistical models through
which clients can measure and grow their
business.
Expertise in
• Digital Media
• Finance/Insurance
• Travel & Logistics
• 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

6. - 6 -
What Aaum can offer
Performance
analysis
Recommendation
engine
Insightful
dashboard
Advanced
Analytics
Experience the magic by clicking our Genie

7. - 7 -
Performance analysis at various levels are vital to the success
Performance
analysis
Recommendation
engine
Insightful
dashboard
Advanced
Analytics
COLLECT
Free
registrants
Expresses
interest for
paid
Paid registrants
Churn
Churn
Feedback
GENERATE
Registrants
Right KPIs tied to right customer profiles at right time
Partially completing
the free profile

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Recommendations based on the learnings from the
performance analysis
•What approach for what candidate profile
• Conversions (Likelihood to make the payment)
• Targeting right prospects
• Page access
• Time spent on tasks
• Submission of horoscopes, photos, references, etc
• Time spent on profiles
• Responses, acceptance, rejects, interests
•Advise/empower the “personal relationship manager” to adopt
appropriate preventive/corrective actions
• Kiosk/Center level recommendations
• What profiles to approach for quick conversions
Performance
analysis
Recommendati
on engine
Insightful
dashboard
Advanced
Analytics

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Insightful dashboard to improve the business
• Monitoring the candidate specific score levels
•Make actions from it
• Categories
• forming elite communities IIT, IIM, etc
• IT software developer from top B schools with eng
Background
• Caste/Sub caste/Communities
• Category performance vs overall performance.
• Overall performance to median (Bellcurve)
• Trend analysis
• and many …
Performance
analysis
Recommendatio
n engine
Insightful
dashboard
Advanced
Analytics
Strategic partnership with
• state of the art reporting solutions

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Advanced analytics to illuminate insights from the data
Performance
analysis
Recommendation
engine
Insightful
dashboard
Advanced
Analytics
• Predicting prospect’s willingness to continue
• Churn
•Acquisition
• Cohort analysis to know the effectiveness of the engagement
• Planning and Scheduling promotions/events
• “Generic online Sunday meetings” vs Café Coffee Day meetings for a few”
• Building models that watch for thresholds
•Early alerts i.e. keeping track of attrition
• Remove fake profiles
• Based on advanced algorithms

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Free state activity - approach
Free period is computed, which is the difference in days between the next payment date and previous expiry date.
Free period is bucketed into the levels,
• 0-15 days(level 1), 15-30 days(level 2), 30-60 days(level 3), 60-365 days(level 4), >365 days(level 5).
Activities are classified into 6 broader categories,
Login
Basic profile modification
• Assured contact, Birthday offer, Family info modification, Hobby modification, Modification screening, Profile modification
Advance profile modification
• Add photos, Add reference, Horoscope generation, Ignore, Mobile alert registration, Profile delete
Match view
Match initiation
• Contact history, Filter, Match watch, Photo match watch
Match communication
• Block, Horoscope request receive, Horoscope request sent, Online payment failure, Personal message receive, personal
message sent,, Phone view request receive, Phone number request sent, Interest receive, Reference request receive, Reference
request sent, Sent interest, Voice message receive, Voice message sent, Phone number request receive

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Free period – looking into the distribution(days) & Customer segments
Free period is the difference in days of previous expiry
date and next payment start date.
The above plot gives the distribution of the free period, the
free period is ranging from a min of 2 days to max of 1563
days, with an average free period of around 130 days.
The second plot gives a better view of the distribution.
Free period of the customers been divided into
- 0-15 days (level1), 15-30 days (level2), 30-60 days
(level3),60-365 days(level4), >365 days (level5)
No free period in the level4, i.e, 60-365 days.
The plot gives us the proportion of the customers coming
under each level of the free period.
Around 80% of the customers became paid members
with in 2 months of free period.

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Activity in the free period levels – proportion of the customers
Login and Match view are umbrella activites, hence not considered while dwelling deep into the activities.

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Activities, sub activities in the free period
The above plot is the broad activity type and underlying activity view.
Around 88% of the match initiation type is
due to the photo match activity
The activities – personal msg receive, receive
interest and sent interest account to almost 71.5%
of the match communication activity category
Add photo accounts to 60% of the advance
modification
Profile modification accounts to 57%
of the Basic modification

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Free period levels – various activities
We have come up with a metric – Expected activity per day, to measure the activity of each customer
coming under various free time levels.
The metric is the ratio of the no.of times the activity happened to the free time days per each customers
each activity.
This metric is calculated at the broader activity type and at even each of the activities.
The following slides will help in understanding the expected underlying activity distribution over the
different free time levels.
Activity period in days, calculated as the difference from next payment date and activity date.
Further, the activity period is divided into three phases, namely “Initial phase”, “Mid phase”, “Near
payment phase”
Initial phase – first one-third of the activity period
Medium phase – second one-third of the activity period
Near payment phase – last one-third of the activity period
Further dwelled deep by looking into 5 parts of the activity period categorised as “Phase 1”, “Phase 2”,
“Phase 3”, “Phase 4”, “Phase5”

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Activity phases in the free period (0-15 days) – various activities
The free period is categorised as “initial
phase”, “mid phase”, “near payment
phase”,
There is sudden dip in the average
expected underlying activity from the
second phase to the third phase which is
the near payment phase and this tells us
that the underlying activity has been more
performed more in the start and mid phrase
of the free period.

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Activity phases in the free period (15-30 days) – various activities
The free period is categorised as “initial
phase”, “mid phase”, “near payment
phase”, which are the 1/3rd, 2/3rd & 1 of
the free period.

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Activity phases in the free period (30-60 days) – various activities
The free period is categorised as “initial
phase”, “mid phase”, “near payment
phase”, which are the 1/3rd, 2/3rd & 1 of
the free period.

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Activity phases in the free period (>365 days) – various activities
The free period is categorised as “initial
phase”, “mid phase”, “near payment
phase”, which are the 1/3rd, 2/3rd & 1 of
the free period.

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5 Phased activity period, free period (0-15 days) – various activities
The free period is categorised as “phase1”,
phase2”, “phase3”, “phase4”, “phase5”,
which are the 1/5th, 2/5th 3/5th, 4/5th,1 of
the free period.

21. - 21 -
5 Phased activity period, free period (15-30 days) – various activities
The free period is categorised as “phase1”,
phase2”, “phase3”, “phase4”, “phase5”,
which are the 1/5th, 2/5th 3/5th, 4/5th,1 of
the free period.

22. - 22 -
5 Phased activity period, free period (30-60 days) – various activities
The free period is categorised as “phase1”,
phase2”, “phase3”, “phase4”, “phase5”,
which are the 1/5th, 2/5th 3/5th, 4/5th,1 of
the free period.

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5 Phased activity period, free period (>365 days) – various activities
The free period is categorised as “phase1”,
phase2”, “phase3”, “phase4”, “phase5”,
which are the 1/5th, 2/5th 3/5th, 4/5th,1 of
the free period.

24. - 24 -
Basic modification, underlying activities – (0-15 days)
Further looking into the underlying activites of each broader activity classification. The above plot is the distribution of the various expected
activity distribution over the three phases of the free period of the activity type “Basic profile modification”.

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Advance modification, underlying activities – (0-15 days)
The above plot is the distribution of the expected activities distribution over the three phases of the free period of the activity type “Advance
profile modification”. Here we can see the average add photos, add referance, and ignore are high in the mid phase.

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Match initiation, underlying activities – (0-15 days)
The above plot is the distribution of the expected activities distribution over the three phases of the free period of the activity type “Match
initiation”. Here we can see the average of expected contact history and match watch are stable in the first two phases and dropping in the
third phase. Photo match watch average expected activity is dropping down over the phases.

27. - 27 -
Match communication, underlying activities – (0-15 days)
Most of the activities remained stable over the three phases except for the activity interest receive
and sent interest where the average dropped in the near payment phase.

28. - 28 -
Basic profile modification, underlying activities – (15-30 days)
Basic profile modification, 15-30 days, the distribution of the underlying expected averages.

29. - 29 -
Advance profile modification, underlying activities – (15-30 days)
Advance profile modification, 15-30 days, the distribution of the underlying expected averages.

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Match initiation, underlying activities – (15-30 days)
Match initiation, 15-30 days, the distribution of the underlying expected averages.

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Match communication, underlying activities – (15-30 days)
Match communication, 15-30 days, the distribution of the underlying expected averages.

32. - 32 -
Basic profile modification, underlying activities – (30-60 days)
Basic profile modification, 30-60 days, the distribution of the underlying expected averages.

33. - 33 -
Advance profile modification, underlying activities – (30-60 days)
Advance profile modification, 30-60 days, the distribution of the underlying expected averages.

34. - 34 -
Match initiation, underlying activities – (30-60 days)
Match initiation, 30-60 days, the distribution of the underlying expected averages.

35. - 35 -
Match communication, underlying activities – (30-60 days)
Match communication, 30-60
days, the distribution of the
underlying expected averages.

36. - 36 -
Basic profile modification, underlying activities – (>365 days)
Basic profile modification, >365 days, the distribution of the underlying expected averages.

37. - 37 -
Advance profile modification, underlying activities – (>365 days)
Advance profile modification, >365 days, the distribution of the underlying expected averages.

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Match initiation, underlying activities – (>365 days)
Match initiation, >365 days, the distribution of the underlying expected averages.

39. - 39 -
Match communication, underlying activities – (>365 days)
Near payment phase Near payment phaseNear payment phaseNear payment phase
Near payment phase Near payment phase Near payment phase Near payment phase
Near payment phaseNear payment phaseNear payment phase
Near payment phase Near payment phaseNear payment phase
Match communication, >365 days, the distribution of the underlying expected averages.

40. Case studies
Analytical Advantage Using Mathematical modeling

41. - 41 -
Project Context: Our client, a US based media and content platform firm for engaging the audience with the most money and
influence online with more than 1,000 publishers as partners. The client approached Aaum to reap analytical insights from
their humongoustransaction data
AAUM’s Contribution:
• Devised and tracked the Key proportions and Key Performance across the sites and publishers
• Achieved “performance scores” to qualify the site/campaignperformance characteristics(CTR, Margin, CPM and Delivery rate) and got validated.
• Extended the scoring technique to derive segment specific indices and compared the peer campaigns
• Root cause analysis: Inspection of site performance attributes of the critical campaigns
Case Study : Campaign and Publisherscoring for a major US based digital media &
content platform firm
Sample Deliverables
… Key proportions and Key Performance … qualifying the performance by scoring
… scoring is extended further to derive segment
specificindices and compared with their peers
… acid test on our scores
Root cause analysis of the critical campaigns

42. - 42 -
Project Context:
Our client, a national television network approached us to derive analytical insights by slicing and dicing
multiple scenarios to help the top management to arrive at actionable results.
AAUM’s Contribution:
• Understanding of target audience, frequent respondents and provision to reward the most loyal respondent (Integrated over a period of time)
• Respondent behavioral characteristics - by age group, gender, voice/non voice
• Media campaign/promotionsefficacy- What time is better, Which channel is better?
Case Study : Analytical support for a major television network
Sample Deliverables
… extracted insights by
dimensional analysis
… chennai’s female population
are older than other vital
circles
….analysis strengthened the
media channel to reward the
loyal customers.

43. - 43 -
Project Context:
Our client, a HR consulting firm, approached AAUM to develop comprehensive metrics which could be
standardized across its clients
AAUM’s Contribution:
• Developed standard metrics that could be rolled out across to the clients of the consulting firm
• Performed interaction analysis for ‘key metrics’ to derive a holistic understanding
• Developed predictive models for some very useful parameters
Sample Deliverables
Case Study: Developed KPI’s and Predictive Analytics for a HR Consulting firm
Standardized metrics were
defined and developed
Interaction analysis of key
metrics was performed and
insights were derived
Predictive models were built
using Random Forests for key
parameters

44. - 44 -
Project Context:
Our client, a major ticket booking company approached AAUM to provide comprehensive analytical insights to
help the top management to arrive at actionable results.
AAUM’s Contribution:
• Developed RFM metrics to help the management to effectively segment customers and reward them.
• Performed cohort analysis to qualify the churning, effective engagement with agents.
• Qualified traveler’s behavioral patterns on routes, regularity, age, gender, per ticket value, lead days, etc.
• Developed dynamic pricing strategy based on the unfilled inventory levels.
Sample Deliverables
Case Study: Analytical support for a major ticket booking company
Margin analysis … Cohort analysis to qualify churn & engagement disengagement levels …
RFM … acquisition …

45. - 45 -
Project Context:
Our client approached AAUM to offer effectively qualify their business operations with insights from data analysis.
AAUM’s Contribution:
• Devised methodology to effectively qualify the payment cycle, ageing metrics, outstanding from customers.
• Performed credit limit analysis, qualified credit consumption patterns and suggested efficient methodologies.
• Performed fare analysis to optimize the travel offerings to customers.
Sample Deliverables
Case Study: Analytical support for the largest integrated travel and travel related
financial services company
lead analysis on ticket fare of
airline travel …
promptness in payments …credit consumption and breaching
patterns …

46. - 46 -
Project Context:
AAUM offered analytical support to a leading global lodging solutions company to leverage their business.
AAUM’s Contribution:
• Slicing and dicing of data to understand customer’s hotel booking preferences qualified on savings, effective
rate and net paid.
Sample Deliverables
Case Study: Lodging analytics for leading global lodging solutions company
Trend analysis to understand the effective rate pattern in
a year …
effective rates over customer preference, location,
position, etc …

47. - 47 -
Project Context:
Developing Business intelligence and analytical capabilityfor a leading financial service company
AAUM’s Contribution:
• Analyzed current BI maturity with comprehensive data analysis of the customer datum collected by our client
• Evaluated and suggested comprehensive plan for BI maturity
• Identified as strategic partner to perform predictive analytics in multiple phase spanning two years
Sample Deliverables
Case Study: Business intelligence for a leading financial services company

48. - 48 -
Project Context:
Research project funded by the company to devise a performance dashboard on various attributes for a movie to predict the
success at the Box office.
AAUM’s Contribution:
• Team AAUM extracted and analyzed the information from the web sources like YouTube and twitter to achieve the specified Business Objectives
• Developed dashboards for various attributes and analyzed the trends in the sentiment scores by using clustering, classificationtechniques.
Sample Deliverables
Case Study :
4
-
- 7 -
Case Study: Measuring the movie performance by mining social media sentiments

49. - 49 -
Project Context:
Our client approached us to develop a framework to analyze the transaction data of their customers and tie these insights with loyalty
card program and reward their customers by their preference and loyalty factor.
AAUM’s Contribution <Pilot demo>:
• Mined associationrules to provide strategic insights for cross-sell/up-sell opportunities
• Framed out loyalty management model for the client.
Sample Deliverables
Case Study: Market Basket Analysis and Loyalty program

50. - 50 -
Project Context:
Our client, police Department archived the crime data for the past ten years and approached AAUM to deliver
insights by performing appropriate analytical techniques.
AAUM’s Contribution:
• Discovered hotspots of crimes by cluster analysis of the historic data.
• Trend analysis of different crimes
• Mining insights from the police records by text mining
• Derived KPIs for crime department to monitor the crimes.
Sample Deliverables
Case Study: Crime Analytics for Tamil Nadu State Crime Records Bureau
Hot Spots found out for crimes in Chennai city.
Trend movement for each type of Crime
Mining insights from the comments recorded

51. - 51 -
Project Context:
Developing Business intelligence and analytical capabilityfor a leading rural bank
AAUM’s Contribution:
• Analyzed current BI maturity with comprehensive data analysis of the customer datum collected by our client
• Evaluated and suggested comprehensive plan for BI maturity
• Identified as strategic partner to perform predictive analytics in multiple phase spanning two years
Sample Deliverables
Case Study: Predictive model to grade the performance of DCCBs

52. - 52 -
Project Context:
Our client has collected valuable data through their Kiosks and wanted to derive insights which can identify new potential business
for rural India
AAUM’s Contribution:
• AAUM profiled the villages and identified clusters
• Thorough analysis done for seven villages in three districts based on income slabs adopted from NCAER & Mckinsey report
• Identified new businesses which would flourish once the villagers migrate to higher income slabs
Sample Deliverables
Data Management: Seamless and secure data transfer using client infrastructure. More than 20 MB of raw data was
cleansed and analyzed using tools available in excel.
44.79%
52.56% 51.23% 48.72% 45.70% 42.59% 41.10% 36.37%
2.98%
1.74% 2.83% 2.59% 2.81% 3.28% 3.26%
2.87%
12.56%
13.19% 10.63%
11.64%
12.09%
13.10% 14.22%
13.52%
7.00%
11.18%
7.04% 8.24%
8.33%
6.45% 4.81%
1.21%
5.26%
8.13% 8.57% 9.62%
12.12% 15.31%
18.65%
17.48% 16.77% 17.94% 18.92% 18.11%
24.35%
3.38% 0.50% 2.66% 3.49% 3.50% 3.54% 3.18% 3.02%
6.35%
15.57%
18.23%
Average Deprived
low
income
Deprived
- Lower
middle
income
Deprived
- middle
income
Deprived
- upper
middle
income
Aspirers Seekers Strivers
Healthcare
Education & Recreation
Communication
Travel
Housing and Utilities
Apparel
Food, Beverages and Tobacco
33.87% 35.77% 32.67% 32.43% 35.14%
24.24%
0.00%
36.89% 39.33%
36.67% 32.43% 27.03%
27.27%
20.00%
24.92% 20.29%
26.67% 32.43%
29.73% 48.48%
60.00%
3.13% 3.35% 3.00% 1.35%
5.41%
0.00%
20.00%
0.42% 0.67% 0.00% 0.00%
0.00% 0.00%0.33% 1.35% 2.70% 0.00% 0.00%0.43% 0.84% 0.00% 0.00% 0.00% 0.00% 0.00%6.35% 0.00%0.32%
Average Deprived
low
income
Deprived -
Lower
middle
income
Deprived -
middle
income
Deprived -
upper
middle
income
Aspirers Seekers
Never visited doctor
Ayurveda siddha doctor
VHN
Private Clinic
Private hospital
Government hospital
PHC
22.32%
51.82%
32.63%
32.73%
15.70%
8.41%
7.14%
2.73%
3.77%
17.89%
3.95% 0.45%
Income Slab (value in %)-
Allivilagam
Number of house holds in
Allivilagam
Seekers (Rs 200000 - 500000)
Aspirers (Rs 90000 - 200000)
Deprived - upper middle income (Rs
62000 - 90000)
Deprived - middle income (Rs 45,000 -
62000)
Deprived - lower middle Income ( Rs
22500 - 45000 )
Deprived - Low Income (<Rs22500)
More than 50% of
villagers in Allivilagam
earn less than Rs.
22,500 per annum
As the villagers start earning
more they will spend on
communication and health
care ….
….and they prefer private
hospitals.
Case Study : Analytics on data collected by a rural research firm

53. - 53 -
Project Context:
Our client approached AAUM to build and implement analytical models for their Healthcare Claims Intelligence
and Insights solution that was targeted at US Healthcare providers
AAUM’s Contribution:
• A workflow and architecture for the claims engine was designed after interactions with the client
• Comprehensive collection of data from the client was undertaken to frame the input for the model
• Based on the design and data, the model was built with flexibility to be customized across client
requirements
Sample Deliverables
Case Study: Building Claims Analytics solutions for Healthcare
Workflow and architecture
of the claims engine were
designed
Claims, Provider, Billing team and
call data were used as input
variables to build the model
The outcome of the approach was a
model which could be customized
as per client requirements

54. - 54 -
Sample Deliverables
Case Study: Credit scoring framework for Micro Finance Institutes (MFI)
Project Context:
Our client wanted Aaum to evaluate and identify a suitable credit scoring framework for scoring MFIs
AAUM’s Contribution:
• Aaum presented a white paper evaluating various credit scoring methodologies
• Plan in progress for solution implementation.

55. - 55 -
Project Context:
Our client approached Aaum to build cost-effective analytical dashboards to reap the benefits of analytical
insights for their business activities.
AAUM’s Contribution:
• Customized analytical dashboards specific to client business
• Dashboardand reports developed and customized specific to roles.
• Appropriate KPIs and metrics generated specific to the client business.
• Ability to store reports in multiple formats
Sample Deliverables
Case Study: Business intelligence dashboard
Dashboard and reports were
developed/deployed specific to
roles …
Enriched with reports, KPIs and
information as per user
privileges …
Multiple possibilities to store
content in the format desired
by the business …

56. - 56 -
Project Context:
Aaum is developing the individual credit scoring model in partnership with NABARD, National banks,
Government, NGOs to grade individuals/SHGs.
AAUM’s Contribution:
• AAUM team simplified the process with no physical requirement to travel physically to SHG/NGOlocations.
• The team is devising credit score algorithms and configured in the server to perform automatic scoring.
• The entire model is getting deployed in the mobile phone to facilitate quick reach in the remote places.
Sample Deliverables
The system would captures all receipts (for the money collected from individual members)
Case Study: Design & delivery of innovative financial products for empowering SHG

57. - 57 -
Project Context:
Our client wanted AAUM to build geographical dispersion of business risks in India
AAUM’s Contribution:
• AAUM identified the variables needed to build risk at a location and created demographic risk framework
• AAUM used demographic risk frame work and identified risks for 60 districts in 18 states in India
Sample Deliverables
Case Study: Geographic Dispersion of Business Risks for a financial institute