The banking and financial industries cover a wide range of organisations. However, this sector is one of the most data rich sectors out there and stands to benefit greatly through advancements made in the field of Data Science.

Cross Selling is one area where this industry enlists the help of big data. Having access to large amounts of customer data can allow a company to make use of predictive analytics to provide recommended products a customer might be likely to purchase. For example, identifying credit card customers who are more likely to take up personal loans.

This same customer data can also include usage data. This can allow organisations to use Data Science solutions to help with Risk Analysis and catch fraud. When even a 1% increase in catching fraudulent transactions can result in saving millions it is easy to understand why companies put so much effort into obtaining the best solution possible. An example of this is anomaly detection and scoring for credit risk using deep learning algorithms.

Similar methods of deep learning can help with Anti-Money Laundering (ALM) where pattern recognition deployment can identify with great accuracy these sorts of transactions.

As with many industries attracting and keeping customers is vitally important, therefore with all the data available to them Banking and Financial Services organisations can make use of Segmentation Analysis to tailor product development and customer experience to combat churn rates.

Even using Data Science when it comes to trading is an area that has gained popularity rapidly in recent years. Advanced analysis and predictive algorithms are used to forecast markets, develop high frequency trading strategies and build long term investment portfolios.