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 institutionsâ 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.
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309 Assets
Gathering relevant datasets
with the help of our sponsor
who has the domain lens
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
30. Fraud is relatively
more correlated
with internal
factors such as
collection mode
and loan oïŹcer
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
37. Recommendations
â Scan copies of physical application form
for cross referencing with any data
validation
â Ensure consistent data types upon entry
through text, numeric or list ïŹelds and
restricting character inputs (e.g.
punctuation marks, spaces)
Application Sourcing
38. Recommendations
â Identify duplicate or related entries such as
names, contact number and addresses of
clients to monitor family relations or relatives
in the program
â Record sources or channels of application to
relate to other clients, chairwoman or loan
oïŹcers
Application Sourcing
39. Recommendations
â Call contact numbers provided by client to
verify loan application
â Conduct ïŹeld validation and adding geotags
for clientâs business pictures
â Flag incomplete requirements uploaded in
the system to check validity of application
Credit Evaluation Process
40. Monitor employee transactions and
collections
â Create a watchlist of Loan OïŹcers or Branch
Manager based on volume of transactions
Recommendations
â Apply stringent standard for loan disbursement
(e.g. signatory, document needed such as IDâs)
Loan Disbursement Process
41. Recommendations
Build data dictionary and data schema
â 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
43. CHALLENGES SOLUTIONS
Domain expertise
Handling large
datasets
Questions and
ClariïŹcations
Disaggregation and
constraints
Data issues and
deïŹnitions
Consultations
with sponsor
Timeframe Agile method