Microfinance institutions are recognized as an effective method of directly improving the lives of those who are most in need as it provides financial services such as loans to poor families. However, one of its obstacles in achieving its objectives to service the poor and broaden financial inclusion are fraudulent activities that are a threat to the institutions’ long-term sustainability. Through proper dashboarding and early investigations of cases with identified deviations of key transactions, the capstone of Team Women5Dev composed of Rochelle Derilo, Jessa Gavilla, Gizelle Nacor, and Patricia Pangatungan used analytics as an aid for cross-functional fraud investigation for a microfinance institution. With appropriate levels of monitoring and tracking, now microfinance institutions can quickly point out potential fraudulent activities for improved risk assessment and operations. -------------------------------------------------------------

1. FRAUD DETECTION
FOR MICROFINANCE
Team Women5Dev

2. 71 percent of
Filipino adults are
unbanked
The unbanked
represent 51.2 million
adults in 2019
47.7 percent of
Filipino households
are low-income

3. MICROFINANCE INSTITUTION
provides small business owners who don’t have access to
traditional financing from major institutions access to capital

4. Provide access to financial services
Aim to elevate the lives of its clients
Push for women empowerment
MICROFINANCE INSTITUTION

5. Someone always ruins it for
everybody

6. A deception deliberately practiced in
order to secure unfair or unlawful
gain or causing loss to another party
FRAUD

7. FRAUD TRIANGLE

8. How can we prevent fraud?
Business Problem

9. Methodology

10. Analytics Process

11. Data Catalog
Exploring
the data
Business
Operations

12. 690.39 MB
306.93 MB
1.27 GB
10.03 GB
6.71 GB
40 KB
88.43 MB
8.11 KB
2.9 MB
309 Assets
Gathering relevant datasets
with the help of our sponsor
who has the domain lens

13. Discovering relationships
across different datasets and
learning about their systems.

14. New tables ready for Analysis
...
Table1 Table2 Table5 Table6
Creating main
tables for analysis
Cleaning and Transforming Data

15. Exploratory Data Analysis and
Data Visualization
Using the newly created tables/analytical base tables,
insights were derived using the following tools

16. Insights

17. MILLION PHP
in losses due to fraud
9.8

18. 68.18%
of the total amount have
yet to be recovered

19. COMMON FRAUD SCHEMES

20. Majority of fraud cases fall under
Undeclared Collection, Loan Ride, and Falsification of
Documents

21. Most staff involved in
fraud includes Loan
Officers and Branch
Managers

22. Walk-in payments
were remitted during
weekends when
centers and branches
are closed

23. Walk-in
Payments are
remitted in some
months only

24. Unusual number of members
from different regions use the
same contact numbers

25. Analytics Solution: Dashboard

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30. Fraud is relatively
more correlated
with internal
factors such as
collection mode
and loan officer
involved
LOANOFFICER
LOAN OFFICER
LOANID
CONTACT NUMBER
LOAN PRINCIPAL
TOTAL INST NO
NO OF INST
COLLMODE
PAYBALANCE
FRAUD
LOANOFFICER
LOANID
CONTACTNUMBER
LOANPRINCIPAL
TOTALINSTNO
NOOFINST
COLLMODE
PAYBALANCE
FRAUD

32. Recommendations

33. Improve Data Collection
Data Quality
Paper Database Analytics
Real insights can only come from good data.

34. Recommendations
Integrate near real-time dashboard with
the new system
● Sustain efforts to utilize monitoring tools
for decision making

35. Recommendations
Application
Sourcing
Credit
Evaluation
Process
Loan
Disbursement
Process
Establish good data capture, validation and
preparation per process

36. Recommendations
Payments
Collection
Savings
Withdrawal
Establish good data capture, validation and
preparation per process

37. Recommendations
● Scan copies of physical application form
for cross referencing with any data
validation
● Ensure consistent data types upon entry
through text, numeric or list fields and
restricting character inputs (e.g.
punctuation marks, spaces)
Application Sourcing

38. Recommendations
● Identify duplicate or related entries such as
names, contact number and addresses of
clients to monitor family relations or relatives
in the program
● Record sources or channels of application to
relate to other clients, chairwoman or loan
officers
Application Sourcing

39. Recommendations
● Call contact numbers provided by client to
verify loan application
● Conduct field validation and adding geotags
for client’s business pictures
● Flag incomplete requirements uploaded in
the system to check validity of application
Credit Evaluation Process

40. Monitor employee transactions and
collections
● Create a watchlist of Loan Officers or Branch
Manager based on volume of transactions
Recommendations
● Apply stringent standard for loan disbursement
(e.g. signatory, document needed such as ID’s)
Loan Disbursement Process

41. Recommendations
Build data dictionary and data schema
● Have common reference on the datasets
across the organization. This may be relevant
for fraud analysis to allow easier
inter-department collaboration for fraud
investigation

42. Challenges and Solutions

43. CHALLENGES SOLUTIONS
Domain expertise
Handling large
datasets
Questions and
Clarifications
Disaggregation and
constraints
Data issues and
definitions
Consultations
with sponsor
Timeframe Agile method

44. Jessa Gavila
Jessa.Gavila@ftwfoundation.org
Roch Derilo
Rochelle.Derilo@ftwfoundation.org
Gizelle Nacor
Gizelle.Nacor@ftwfoundation.org
Patcha Pangatungan
Patricia.Pangatungan@ftwfoundation.org
Women5Dev

45. FRAUD DETECTION ANALYSIS
Team Women5Dev