Introduction to Big Data in Banking
The Banking Sector is the fuel which keeps the Economies, Nations and Organizations running. It also generates astronomical amounts of data every second. Every transaction leaves a footprint and creates data which was considered static and only useful for Auditors for the purpose of Accounting and Auditing. However, with the emergence of Big Data technologies in other areas like Healthcare began showing its true potential, we started using such “worthless” and “stale” data into those systems and began to truly see the potential of financial insights which could be used for many purposes. Therefore, there is an untapped potential that Big Data in Banking possesses, and we’ll try to find out such implications and advantages of how it works and the possibilities which could be explored.
In 2008 due to the subprime lending crisis in the US, the world economy was in turmoil which also demonstrated two things,
- How connected the world was and how a crisis which started in one country can quickly and negatively impact all major economies in the world and shattered world banking and financial markets.
- How desperately we needed a technology that could help us identify bad/Subprime loans, structural shift in banking stances with respect to lending, Customer profiling, etc.
Big Data along with Business Intelligence technologies helped in this endeavour and facilitated the Banking and Financial institutions to challenge the status quo back in 2008 and started the emergence of Big Data in Banking Sector. Banks use Big Data and BI technologies like Hadoop and RDBMS in all their processes and changed the face of banking for all and for good. From Digitising all banking processes to converting Developing economies from Cash Heavy transactions to Digital transactions, Big Data has helped shaped Organizations and Institutions across the world.
Uses of Big Data in Banking Sector
Some of the areas in which Big Data is used in the Banking Sector:
1. Customer Profiling
Big Data helps in profiling customers for the Banking institutions which enables them to cater to individual customers based on their banking history and transactional patterns over the time they have been with the bank. This enables them to make customised plans and solutions for their customers. This gives great impetus to customer experience and helps banks distinguish themselves and retain their customers. Banks can also push different products to different customers based on their profiles.
2. Fraud Detection
By analysing data and with the help of Statistical computing banks can detect Fraud even before it occurs. With unique fraud detection Algorithms to track and compute spending and other behavioural patterns, one can identify and gauge if a person is on the brink of financial ruin and can be enticed to defraud banking institutions. Various Banking Institutions like Retail Banks, Investment Banks, NBFCs, Private Equity’s and others have a dedicated Risk Management department which heavily relies on Big data and Business Intelligence tools.
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3. Lending Decisions
One of the most crucial decisions in the Banking sector is that of lending. Selecting the right customer who is both creditworthy and financially sound to pay off debt is of utmost importance. Also, traditionally banks used to rely on Credit rating agencies to gauge the creditworthiness of a customer and that could not tell the whole story as it considered a certain rationale and ignored others. With access to newfound insights from big Data analytics banks can consider other factors like spending habits, nature and quantum of transactions, etc to come to a decision whether to lend a customer. This has broadened the horizon for bankers and Financial Institutions to have more data and knowledge at their disposal and based on the customer’s risk profile, take appropriate decisions.
4. Regulatory Compliances
With Big Data Analytics and BI tools, keeping records and complying with regulations becomes extremely effective and efficient. From various taxes to keeping records with the Central banks, they can effectively manage and track all these regulatory procedures. With legacy systems, it was very effort and labour intensive to make sure the compliances are in place and dealt with accordingly, however with BI tools it becomes extremely easy as all the information is concisely put together in a way never possible before, making it easier for the decision-makers to comply easily. Moreover, they themselves when programmed correctly can manage such compliances thereby mitigating risks of error, fraud by human intervention.
Cyber attacks and online financial frauds, embezzlement is extremely common and even the best of organizations in the world are facing this problem. We have seen many big organizations and especially banking institutions come under such cyber-attacks where it’s not just the money but other information about the customers gets stolen.
With the help of Big Data and AI tools, Banks can setup robust internal control systems as sometimes these activities could be performed by someone from the inside of the organization and with Advanced Algorithms, they can track customer behaviour. Also, when needed, in case of financial terrorism they can actively cooperate and share insights gained from their BI tools and Big Data Analytics with governmental agencies to counter such risks.
There are countless other examples where Big Data in Banking has played and will play a big role in the coming years making our Banking systems more robust and stronger. Most of the Big banking institutions around the world after 2008 banking crisis had started with Data Science teams but often outsourced it as they didn’t know the true potential of Big Data and how it can help them. However, all major institutions now have their own in-house teams who are constantly developing and implementing new processes using Big Data Analytics and BI tools.
This is a guide to Big Data in Banking. Here we discuss the introduction to Big Data in the banking sector along with the uses of Big Data in Banking Sector which includes Customer Profiling, Fraud Detection, etc. You may also look at the following articles to learn more –