Irum Amjad Marko B Popovic Oscar Moll

Problem  Traditional customer account information do not fully summarize the customer spending patterns  E.g. most spent on Items, change in habits etc  Question: Is there more general explanation to the phenomenon of customer transactions?

Goals  Identify modes of pathological spending behaviors from current and historical data  time patterns, resource allocations  Predict future spending pattern  Plan and assist customers to prevent delinquency  Extract, classify and visualize spending over long time periods to assist both bank customers and employees

Proposed Approach Classify user resource allocation over a time frame Determine the root of main expenditure for each class Design optimal schedule and allocations of financial resources for each class(future work) Create Alerts system to help individual users spend more responsibly(future work)

Information needed  Healthy customer accounts data  Data on specific allocation of resources  Track group of current users if historical data unavailable or does not exist

Project Details  The customer profile is defined for each month  The horizontal bars represent the dollar amount which the customer spends based on a monthly pattern.  Then in each criteria a bubble highlights the most spent on Items.  The different colors in the Dollar amount bar are representative of the different criteria's

Customer Profile January

Customer Profile February

Customer Profile March

Customer Profile April

Customer Profile May

Customer Profile June (Current)

Behavior Pattern Travel Health Real estate

Bank can collaborate by  Health:  Helping the customer look into different loans or health insurance policies for health  Travel:  Provide incentives to collaborate with the Bank of America airline customers  Real Estate:  Look into buying real estate using Bank of America as a third party.

Future Applications  Alert system to warn customers when they exceed the “optimal” budget  Integrate the results with the Google maps to alert users each time a undesirable financial behavior is about to arise  Based on previous transactions associated with certain locations, routes, times

Importance of this approach  Summarizing and classifying customer expenditure can result in the following important steps  Classify customer behaviors using clustering  Identify customer spending locations  Identify customer spending pattern based on time of the year  Create customer guide to help customers with credit and loans based on pattern of transactions

Real Estate  Transaction date: Transaction date:  Place:5 th Ave(  Amount:$50  Time:18:05  Frequency:Alot

Starbucks  Transaction date: Transaction date:  Place:5 th Ave(  Amount:$50  Time:18:05  Frequency:Alot

Macdonald’s  Transaction date: Transaction date:  Place:5 th Ave(  Amount:$50  Time:18:05  Frequency:Alot

Target  Transaction date:  Place:  Amount:$100  Time:18:25  Frequency: rare

American Airlines  Transaction date: Transaction date:  Place:5 th Ave(  Amount:$50  Time:18:05  Frequency:Alot

Massachussettes General  Transaction date:  Place:5 th Ave(  Amount:$50  Time:18:05  Frequency: lot