The importance of Big Data Analytics in the Banking and Financial Services IndustryJuly 29, 2018
In this data-driven world, Data Analytics has become vital in the decision-making processes in the Banking and Financial Services Industry. Investment banking and other businesses wherein, real-time information is used, volume, as well as the velocity of data, has become critical 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 information by Banks and other financial organisations 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 2016, 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 analysing over 1300 unique call features – such as location, background noise, number history and call type – to 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 programme at the European Contact Centre & Customer Service Awards 2017.
Danske Bank uses its in-house start-up, Advanced Analytics to evaluate customer behaviour and determine preferences, as well as to better identify fraud while reducing false positives. Eventually, the implementation of data analytics helped Danske Bank to increase detection rates of new types of fraud by to 40%. Once they added deep learning to the equation, this figure rose to 60%, a significantly considerable improvement over conventional fraud detection method.
JPMorgan Chase also developed a proprietary Machine Learning algorithm called Contract Intelligence or COiN for analysing various documentations and extracting relevant information from them. With the implementation of this system, JP Morgan Chase was able to eliminate an estimated 360,000 hours of work every year which otherwise would have had to be done by financial loan officers and lawyers.
Big Data is also used for personalised marketing, which targets customers based on the analysis of their 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 personalised customer support service. Big Data also helps in Human Resources management by implementing incentive optimisation, attrition modelling and salary optimisation.
The list of use cases implemented in the workflows of the Banking and Financial sector is growing day by day. The massive increase in the amount of data to be analysed and acted upon in the Banking and Financial Sector has made it essential to incorporate 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 personalised and secure banking and financial services to improve customer satisfaction. In this extremely competitive market, it is essential for companies to invest heavily in Data Analytics to continue being relevant and reach the efficiency levels of other companies which have already implemented it.