AI and Machine Learning in Financial Services | HCL Blogs

AI and Machine Learning in Financial Services : Accelerated adoption in the Post COVID new-normal

AI and Machine Learning in Financial Services : Accelerated adoption in the Post COVID new-normal
July 20, 2020

“The COVID-19 pandemic has compelled banks to undergo digital transformation, worth a decade, in a few months flat.”

For financial institutions, the post-COVID-19 new normal marks the end of experimentation with fintech and digital technologies and the beginning of a large-scale transformation, driven by the adoption of new technologies in partnership with technology firms. While previously, banks were convinced that they were advancing in the race to undergo digital transformation in financial services, little were they realizing that these efforts had minimal impact on the strategic direction, customer service and hence, the bottom-line results in most cases. The solutions weren’t a quantum leap towards delivering next-generation financial services. At best, they fit into the existing infrastructure as a workable solution. More often than not, they failed to make a noticeable difference.

The COVID-19 pandemic has compelled banks to undergo digital transformation worth a decade in a few months flat.

With COVID-19, everything has changed. The luxury of experimenting with technology has disappeared overnight. Banks are having to accelerate their digital innovations in every aspect of their business, from sales and marketing to customer service and operations. They are having to reinvent their business models for the post-COVID times. The importance of dramatically shrinking the time, between the generation of a business idea and the delivery of business value, has never been greater.

Recent announcement from Deutsche Bank and Google, highlighting their partnership and its benefits, is a harbinger of things to come over the next few months/quarters and will accelerate similar bank-tech partnerships. LinkedIn posts and tweets from the senior management teams at these organizations show the excitement this partnership is generating.

While the need for transformation has been accentuated, financial institutions are also under immense pressure to continue all critical financial services operations as normal. The need to focus on stability and resilience, while rapidly transforming themselves, has also forced banks and financial institutions to look for solutions in newer technologies that can drive innovation across their business value chain.

The need to reduce manual intervention in operations and hence, improve safety and soundness across the enterprise is driving the adoption of AI and machine learning driven solutions. Banks are hence transforming into digitally driven enterprises, akin to big tech firms and are building capabilities that enable a relentless focus on the customers.

Some areas have seen the early adoption of these technologies, prior to the COVID-19 pandemic, and are now seeing an accelerated deployment of these technologies. Let’s look at a few of them to begin with.

e-KYC with Intelligent Automation and RPA

Intelligent automation and robotic process automation help optimize functions, enhance efficiency, and improve the overall speed and accuracy of the core financial processes, leading to cost-savings.

IA and RPA are being increasingly used to automate the crucial yet repetitive, mundane, and tedious processes that require substantially higher manpower and human work hours. Such processes may include document handling for e-KYC, loan disbursement, loan repayment and regulatory reporting.

Exacto by HCL Tech is a platform enabling a number of such use cases; it runs on a NLP-based engine (Natural Language Processing), which manages, interprets, and extracts unstructured data, such as text information, images, scanned documents (handwritten as well as electronic), faxes, web content, and natural language input to enable improved performance, with a notable reduction in the average handling time leading to superior customer experience.

While intelligent automation has been around for quite a while, tools like Exacto take it a step further, by enabling the system to not only handle but also identify any missing, unseen, and ill-formed data, resulting in near-perfect accuracy and higher reliability.

The intelligent automation solutions reduce the AHT (average handle time) and empower financial organizations to be able to process significantly more inputs, while managing dynamic problems. Such solutions are proving to be beneficial in streamlining processes in financial services that are costly, human intensive and error-prone, and improving customer experience - thereby providing firms with significant competitive advantage.

Improved Customer Support with IDA/Virtual Assistants

IDA (Intelligent Digital Assistant) is a virtual assistant catering to customer needs without having to rely on employees. This AI and machine learning solution provides a simpler solution to increase productivity by minimizing the time and effort spent on generic customer queries.

We have all seen them, in the form of chatbots, in several e-commerce stores that offer live chat; however, in the financial services industry, organizations such as JP Morgan are now also leveraging these bots to streamline their back-office operations and strengthen customer support.

COIN, short for ‘Contract Intelligence,’ runs on a machine learning system that is powered by the bank’s private cloud network. This bot not only automates fitting responses to general queries, but also automates legal filing tasks, reviews documents, handles basic IT requests such as resetting passwords, and creates new tools for both the bankers and clients with more proficiency and less human-error. 

As a matter of fact, in a case study published by the digital magazine Futurism, JP Morgan reveals that leveraging AI and ML in financial services, it has completed 360,000 hours of finance work in just seconds.

Loan Portfolio Analytics

The estimation of creditworthiness is largely based on how likely an individual or business might repay a loan. The ability to determine the likelihood of default underpins the risk management processes at all the lending businesses.

Even with impeccable data, this process of assessment is filled with risks. It is not uncommon to find the data deficient. People and organizations in some cases will (and do) lie.

To combat these limitations, several organizations like Lenddo and ZestFinance are using AI, to determine the creditworthiness of individuals and for risk assessment. Credit bureaus like Equifax utilize AI, machine learning and the power of advanced data and analytical tools to analyze alternate sources, to evaluate risk and gain customer insight.

While in the past, money lenders used a limited set of data such as the salary earned by an individual and their credit scores; organizations have now begun to consider his/her entire digital financial footprint to determine the likelihood of default.

In addition to traditional data sets, this alternative data and the analysis of it tends to be especially valuable in deciding the creditworthiness of individuals, without the conventional records of loan or credit history. Technologies such as artificial intelligence and applied machine learning and financial services are proving to be exceptionally useful in this process.

 Needless to say, in this post-COVID-19 world, the way businesses and clients interact with each other has irreversibly changed. We have seen banks and other financial institutions leveraging technologies like AI, Machine Learning and Intelligent Automation, to improve back office operations for cost reduction while at the same time, deploying technology to significantly improve customer engagement.

A digital transformation in banking and financial services that was in its infancy prior to COVID-19, has picked up steam during the pandemic and will only accelerate exponentially in the post-COVID world, demonstrating the kind of benefits and productivity improvements industries have not seen before.