Changing market opportunities, phenomenal growth in competition, and an empowered customer base are compelling the age old banking industry to renovate itself. In fact the flexibility and efficiency expected from the banking industry today, make such a renovation almost inevitable.
Added to the many challenges banks face is the extraordinary amount of data that banks deal with. This includes both, data from within the organization as well as the World Wide Web.
This data pertains to customers, businesses, processes, employees, and much-much more. All this data is the lifeline for any bank. After all, this data holds the key to a source of insights that determine the bank’s productivity, efficiency, and profits.
So the banks can use all this data to draw insights and consequently make huge gains. Isn’t it? Well, it ain’t that easy. The keyword here, with all this information waiting to be tapped, is ‘unstructured’.
While this mound of data is beyond the scope of human analysis, traditional machine analysis also doesn’t seem to be very helpful in this situation.
Conventional or traditional machine learning algorithms, employed for data analysis, need classified or structured data and aren’t absolutely accurate. The needed solution here is an algorithm that thrives on unstructured/unclassified data, lots of data, and record breaking accuracy.
Deep learning is that solution! Deep learning excels at identifying patterns in unstructured data, be it text, images, sound, or video. Let us see how deep learning therefore, is being used in the banking industry.
Risk Management:
With an exponential rise in regulations post the global financial crisis, risk management has been a major point of focus for banks worldwide. Identifying the risk score of a customer depending upon his salary, nationality, credit history, etc. is of absolute importance to banks.
This information helps them decide what product or service to offer to a particular customer. It also helps them decide the interest rate for a given customer. Deep learning helps banks use multiple data sources and analyze them to deduce the creditworthiness of a customer.
Fraud Analytics:
The banking industry has unfortunately remained a hot favorite with frauds. Fraud losses incurred by banks and merchants on debit cards, credit cards, prepaid general purpose cards, and other such cards issued worldwide reached around $16.31 billion last year.
Deep learning presents probably one of the biggest opportunities to leverage analytics for identifying patterns in data, getting a holistic view of customers, and distinguishing between the fraudulent and normal activity.
Customer Segmentation:
Today bank customers range from the social media generation to the retired population. Their requirement, attitude, and behavior is unique from one another.
This is particularly important in view of the frequent migration of customers from one bank to the other. Deep learning helps banks mine intelligence from the underlying data and segment customers effectively.
Finding New Business:
As more and more people are turning to social media for promoting their business, banks now have access to information about their customers that goes well beyond the data stored on CRM systems. However, the job is not just to gather information from social media but to draw actionable insights from them.
Most of the information on social media is unwieldy and large. This information needs to be checked for distinguishing between the useful and the not so useful information.
Deep learning precisely does this and helps extract meaningful information. This information helps banks target the new age customers who lead a digital life and leave behind digital footprints of their interests and recent purchases on social media.
Performance Evaluation:
Deep learning is being used to scan the employee engagement data, such as emails, chats, blogs, work log, survey etc., to get a clearer picture of the internal performance of a bank.
It also analyzes and compares the performance of different branches of the bank to help banks improvise their performance and save them from closing down.
Banks are driven against an increasingly competitive landscape. Adopting only the best and the latest technologies remains non-negotiable for banks in such a scenario.
Understanding the emerging customer trends, plugging out the smallest of loopholes, handling the large volumes of unstructured data, and improvising their performance will make or break a bank.
Deep learning is the best shot any bank has, to explain the past, understand the present, and predict the future of banking.
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