Financial stability is one of the
major aspects that contributes towards the growth of a country and thus banks
play an active role in economic growth and development of a country through
provision of credit. Financial payment modes such as credit cards, debit cards are
increasingly accepted and used worldwide. The use of credit cards in the
purchase of goods and services has also increased extremely.
Consumers defaulting on their payments have also been increasing
and being a major challenge for bankers as it endangers their system and pose
chances of losses that might be hard to recover from. When someone defaults,
the bank takes a huge loss that can easily add up to thousands of dollars.
Predicting what type of customer has the highest chance of defaulting can help
banks make better decisions about who to give credit to, how much to give and
also provides safety and security.
The major idea behind predicting credit card defaulters is
to protect the economic prosperity of the country, prevent consumer over-indebtedness
and also to prevent credit lenders from granting loans to bad customers and to
avoid giving false rejection to good customers. In order to examine the problem
more deeply, consider the review terms “credit card defaulters”, “consumer
credit market”, “credit lenders”, “credit card debt/facts”.
Also, from a business perspective credit defaults leads to high
borrowing rates and therefore increased cost of doing business. At the same
time, it helps to promote sales, as buying on credit constitutes an enhancement
of the buyer’s purchasing power, thereby increasing demand, turnover, and,
consequently, profitability. From the consumer perspective, availability of
credit increases the purchase convenience and raises the level of consumption
and welfare of the buyer, as he is able to buy and consume now at a level only
feasible at a future higher level of income. On secondary note it helps
consumer avoid the inconvenience and risks of cash-based transactions,
including fraud, robbery, and violence.
In this context this project is aimed at building cloud
based prediction models to discover what type of customers are most likely to
default on their credit card payments based on credit card data. After
analyzing the data and discovering trends, patterns the findings from these
models will be represented using a web-based visualization tool.