Mutual Fund Redemption Model

Problem Statement• Redemption of invested funds by customers is a

scenario in which capital market players such as Securities firms or Mutual Funds have to prepare for and face on a daily basis.India Mutual fund Industry size is $14 Trillion with close to 80% volume traded in Open Ended Fund according to SEBI Report.

Objective• To study Mutual fund market life cycle of a open-

ended fund, and based on their characteristics and transaction history predict Redemption in future(Current Model:-90 days)

• Create Segmentation of Investors on basis of their Behavioural characteristics and Demography

Business Objective• Customer Retention• Cross-sell/Up-sell (secondary)

Analytics objectiveCustomer may Redeem due to following• Lower Returns in portfolio• High Risk due to volatility of market• Poor Fund services• Higher capital taxes and change in interest rates

Data AvailableHistorical Transaction Data

• Transaction type, date, Mutual Fund plan and Schemes• No of units purchased, amount, NAV, Price• Unique Identifier:- ClientCode, CommonClientCode

Customer Data• Demographic details• Account Opening Date, balance, ledger details

Analytics Approach• Data Cleansing:

– Removing outliers

– Removing usage records after Redemption.

• Data Integration:

– For each ID combine data for client, ledger

– Merge dataset with Sensex and NIFTY historical data

– Apply Redemption date if customer has Redeem fund

– Calculate derived variables to capture user behaviour

Data: DiscoveryUnique Client Code vs. Transaction type

Redemption Transaction By Year

Derived Variables

• Scheme Returns• Asset ratio• Beta• R Squared• Scheme Risk• Market returns• Market Risk• Vintage• Broker profit• SIP Tenure• STP Tenure• Transaction count• Age of customer• Switch Out Ratio• Mean NAV

Total Variables calculated: 23

Variables considered in Model: 10

Building ModelEight Year Transaction Data

(2007-15)

60 % Training Data

40 % Testing Data

Validate Data

Total Available Data

Training Data

Testing Data

Validation Data(Aug-OCT)

Logistic Regression

To find predictive variables To predict redemption of fund by

the customer

0

1

No Redemption

Redemption

Model: Setup

Model Summary

Prediction Accuracy• Accuracy:- Test Data:-76 % Validation Data:-71%

Model Application

Predicted

Actu

al

Positive Negative

Positi

veN

egati

ve

TP FN

FP TN

TP - True PositiveFN - False NegativeFP - False PositiveTN - True Negative

0

0

0

0

1

0

Redemption miss

Actual States

Predicted States

1

1

0

1

1

1

Non Redemption State miss

Confusion Matrix

Predicted

Actual 0 1 Sum

0 320060 26480 346540

1 11705 51064 62769

Sum 331765 77544 409309

Predicted

Actual 0 1 Sum

0 18272 524 18796

1 1037 3879 4916

Sum 19309 4403 23712

Test Data Validation Data

Test ROC

AUC:-

Validation ROC

AUC:-

Random Forest

GBM

Future WorkTo optimize Mutual fund sales ,reduce costs, customer acquisition and to enhance customer satisfaction and retention using uplift Modelling technique.Focus on customers who will purchase after price reduction

Cross-Sell Analysis

Hypothesis

Customers with only

EQ

Customers with both EQ

and MF

Characteristics of cross-sell customers

Characteristics of customers with only EQ

The people who bought only EQ but they have similar characteristics (in terms of variables) to the people who bought both EQ and MF, are more likely to buy MF also.

Age frequency maximum between 33 to 43

Approach-1Based on Demographics of customer

Vintage frequency maximum between 4 to 6

Vintage

Graduation has the highest frequency amongst education

Maharashtra has the highest frequency amongst all states

State

Approach-2Based on the activity of customers

Table 1 for customers with Equity or Both EQ and MF

Table 2 for customers with Both EQ and MF

𝐸𝑞𝑢𝑖𝑡𝑦 𝑎𝑐𝑡𝑖𝑣𝑖𝑡𝑦 (𝑒𝑎 )=∑𝑖=1

𝑛

𝑥𝑖𝑛

𝐼𝑛𝑎𝑐𝑡𝑖𝑣𝑖𝑡𝑦 𝑟𝑎𝑡𝑖𝑜 ( 𝑖𝑎 )=∑𝑖=1

𝑛

𝑦 𝑖

𝑛

From customers of table 1 find distance of each customer from the centroid .Based on this distance rank the customers from minimum distance to maximum distance.

Thank You