Banking is usually the slowest industry to respond to innovations – for a myriad of reasons: heavy regulation, risks and stakes involved, and the industry’s fundamental need to hold the public trust.
But, not innovating, when the world is, can also compromise people’s confidence as well as the industry’s stability.
Perhaps this is why it has started to harness the power of information brought by big data, so much so that banking accounted for the highest share of big data revenues and continue to remain among the top in years to come.
The banking industry made the highest investment in big data and analytics in 2018 – with 13.6% of the global share (IDC).
Investment Banking industry can especially benefit by capitalizing on humungous data generated by it, with advanced analytics bringing in the power of predictive research.
Here are 5 use cases –with examples – on how the iBanking industry, and banking in general, is leveraging data to bring about a fintech revolution.
5 Use Cases of Big Data in Banking – With examples
1. Predictive Banking – Axis Bank
The most popular results of predictive analytics are “recommendations engines” that suggest customers the next course of actions they are likely to take, and what they need; based on their previous searches and past behavior analysis. It becomes possible with deep learning and neural networks that micro-segment data across timelines.
For investment banking professionals, predictive analytics translates into, say implementing automation techniques to identify how a financial instrument is likely to perform in the future, based on past trends.
Consider, the case of Axis Bank, a private bank in India. It put in place a robotics process to identify customer behavior and recommend actions (to the bank) to prevent a customer from say leaving a bank. In such a case, banks can pre-emptively offer special promotions to those customers.
2. User Segmentation and Targeting – Barclays
Tapping in big data can help you save from your marketing budget, yet leading high returns. This becomes possible with highly targeted marketing strategies. McKinsey finds data can help organizations make better decisions saving them 15-20% of their budgets for marketing.
The Case of Barclays, a multinational investment banking behemoth headquartered in London, aptly used its consumer data to augment its marketing strategies and fine-tune user segmentation to precisely target customers. It used “social listening” for sentiment analysis to source actionable insights from social networks.
3. Personalized experience – American Express
Customers are looking for tailored and individualized experience – in everything, including banking, especially as millennials and future generations for whom the internet is as normal as anything else. How can banks do it? By using the data to know their users better. From there, they can find new ways to cater to consumer needs.
This approach was taken up by American Express. Its Australian branch leveraged predictive models to forecast their customer preferences and reduce their customer turnover. American Express predicted accounts most likely to get closed in a couple of months.
4. Business Process Optimization with automation – JP Morgan Chase & Co.
McKinsey revealed, 30% of works in a bank can be automated through technology, and the key to it is big data. Leveraging the power of automation through big data, banks can save several amounts, and reduce the margin of error by eliminating the human factor.
JP Morgan Chase & Co, pioneered automation in banking. Currently, it employs many AI and ML programs to optimize its processes – such as algorithmic trading used by its investment banking professionals. LOXM is one such program of JPMorgan Chase that relies on data from billions of transactions to trade equities at maximum speed. COIN is another data-based automation by JP Morgan Chase. It is powered by its private cloud network, to reduce the time needed to review documents – that have reduced from 360,000 hours of work to seconds.
5. Credit decisioning – VisualDNA
Approving loans in seconds, while keeping all security fronts tightly packed, is the dream of most financial institutions. In traditional banking, the loan approval was dependent upon manually driven processes – accumulating information from multiple sources and basing decisions on the data thus received. Indeed, it was time-consuming, slow and rigid.
VisualDNA is a company that is aiding banking and financial services to develop psychometric tests for them to determine customer’s credit risk. Thus, along with automated document processing for quick loan disbursal, psychometric tests can determine if one should be lent.
While most of those in the investment banking and finance sector see the merits of using consumer data for benefiting their business as well as customers; there are concerns around customer privacy, and technical friction stemming from renovating their age-old complex systems.