BANKING

BANKING

DSF (Decision Sciences Factor) uses its Machine Learning capabilities to increase top and bottom line for its Banking customers through gaining competitive advantages, reducing expenses, and improving efficiencies. The current increasing competitive landscape has made it necessary for the Banking sector to optimize all areas of their business from risk analysis and fraud detection to marketing, using data driven decisions that will lead them to increased profitability.


Credit Card Fraudulent Transactions:

The Business Challenge:

Fraudulent transactions are costly, but it is too expensive and inefficient to investigate every transaction for fraud. Some of the other challenges also includes:

Working with large sets of unstructured addresses’ data to map/match different transactions and clients using only names and addresses.

Using the client’s fuzzy matching framework for names and addresses, which cannot provide a single view of customers across different data sources when no unique ID is provided.

The Solution:

Using DSF, you can automatically build extremely accurate predictive models to identify and prioritize likely fraudulent activity. Fraud units can then create a data-based queue, investigating only those incidents likely to require it. The resulting benefits are two-fold. First, your resources are deployed where you will see the greatest return on your investigative investment. Additionally, you optimize customer satisfaction by protecting their accounts and not challenging innocent transactions.


Predicting Next Product to Buy – Cross Marketing Platform

The Business Challenge:

This client recently launched a new type of Pilot account to improve the penetration of a new product within its existing customer base. They asked the Yottaasys team to help them identify the customers most likely to open this new type of account. The client wanted to test the DSF capabilities in a test environment to help drive its cross-sell activities.

This engagement had two objectives:

Develop a model to determine the likelihood of an individual customer opening and funding the new type of account.

Segment the bank’s retail base to effectively target prospective customers for cross-sell activities.

The Solution:

Modern machine learning algorithms can substantially increase accuracy for improving the effectiveness of cross-selling. The NPTB model reduces the waste of poorly targeted cross-selling activities by predicting product each customer would be most likely to buy next. DSF helps you find that statistical method makes little difference in predictive accuracy, with neural nets having a slight edge. A simple random sample to create the calibration database increases predictive accuracy more than a stratified random sample, although the stratified sample may be preferred to avoid underpredicting unpopular products. DSF can also help you explore the potential for incorporating purchase incidence models in the NPTB approach, and find that this potentially enhances the effectiveness of the NPTB model.

Highlights:

Understand the Behavior patterns of the customers by visualizing business rules.

Build highly accurate predictive models without data science expertise

Go from concept to production in minutes.

Results:

Increase Profitability drastically

Increase Conversion Rates

Enhanced Customer Satisfaction

Better Customer Acquisition and Retention.





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