In this data-driven world, Data Analytics has become vital in the decision making processes in the Banking and Financial Services Industry. In Investment banking, volume, as well as the velocity of data, has become very important factors. Big Data Analytics comes into the picture in cases like this when the sheer volume and size of the data is beyond the capability of traditional databases to collect.
Today, data analytics practices have made the monitoring and evaluation of vast amounts of client data including personal and security informant data-driven and other financial organizations much simpler.
There are several use cases in which Big Data Analytics has contributed significantly to ensure the effective use of data. This data opens up new and exciting opportunities for customer service that can help defend battlegrounds like payments and open up new service and revenue opportunities.
For example, in October 2106, Lloyds Banking Group had become the first European bank to implement Pindrop’s PhoneprintingTM technology for detecting fraud. Their technology used AI to create an ‘audio fingerprint’ of every call by analyzing over 1300 unique call features – such as location, background noise, number history, and call type – the o highlight unusual activity, and identify potential fraud.
It cracks down on tactics like caller ID spoofing, voice distortion, and social engineering without any need for customers to provide additional information. Subsequently, Lloyds Banking Group went on to win the Gold Award for ‘best risk and fraud management program’ at the European Contact Centre & Customer Service Awards 2017.
Danske Bank uses its in-house start-up, advanced analytics to evaluate customer behavior and determine preferences, as well as to better identify fraud while reducing false positives.
JPMorgan Chase also developed a proprietary Machine Learning algorithm called Contract Intelligence or COiN for analyzing various documentations and extracting important information from them.
Big Data is also used for personalized marketing, which targets customers based on the analysis of their individual buying habits. Here, financial services firms can collect data from customers' social media profiles to figure out their needs through sentiment analysis and then create a credit risk assessment. This can also help establish an automated, accurate and highly personalized customer support service. Big Data also helps in Human Resources management by implementing incentive optimization, attrition modeling, and salary optimization.
The list of use cases implemented in the workflows of the Banking and Financial sector is growing day by day. The huge increase in the amount of data to be analyzed and acted upon in the Banking and Financial Sector has made it essential to incorporate increase the implementation of Big Data Analytics.
Knowing the importance of data science is crucial in these sectors and should be integrated into all decision-making processes based on actionable insights from customer data. Big Data is the next step in ensuring highly personalized and secure banking and financial services to improve customer satisfaction.