How NLP helps BFSI firms to overcome 6 of their topmost challenges 

When a trend in business is predicted to rise by more than 3000% and produce $7.3 billion worth of cost savings worldwide, over a five-year period, people will take note! After all, who has not observed the exponential rise in interactions with chatbots regarding banking customers in the last couple of years? Chatbots are just one of the many applications of natural language processing (NLP), a subset of AI, significantly impacting various industries, including the BFSI sector. In this blog, let me take you through the top challenges that BFSI firms face and how NLP helps them tackle those. 

Effects of the Pandemic on the Sector 

The shift towards working remotely and an upsurge towards everything online during the pandemic forced many financial services providers to re-evaluate their business models to meet the evolving needs of their customers and employees. Even before the pandemic, fintech organizations disrupted the financial services scenario with agile mobile applications. Although every organization’s scenario and business challenges may be exclusive, most consider AI or precisely NLP as a crucial tool they cannot afford to overlook. 

A sector reinventing itself with NLP 

When it comes to the BFSI sector, banks, brokerages, insurance companies, and financial services organizations, NLP is rapidly emerging as a solution to its perpetual issue of excessive data and insufficient staff. Along with managing straightforward customer service issues and directing consumers to the appropriate department, firms can use NLP to identify bias and fraud, guarantee adherence to strict standards, and yield competitive distinction. 

IDC projects that by 2025, NLP will take the lead in the estimated $120 billion in annual investment in AI in the US. According to Markets & Markets, NLP will account for $35.1 billion in worldwide AI investments by 2026. The financial services sector is anticipated to account for a significant portion of this spending. 

How does NLP support? 

In a nutshell, NLP is software that permits a computer application to understand human language, be it written or spoken. Relying on advanced machine learning tech, involving neural nets, and deep learning, enables interactions between humans and machines- something incomprehensible a few decades ago. At the fundamental level, NLP indicates voice-to-text and text-to-voice capabilities (that have become familiar elements of everyday life, such as interacting with smartphones and smart devices).

Then there is the advanced level of NLP that can execute sentiment analysis, translate one language into another, detect deception on the part of a writer/speaker, and even summarize the text. On top of all, chatbots are a crucial implementation of NLP as the rising number of chatbots is increasingly challenging to differentiate from human agents. As a result, for BFSI firms, NLP tech opens a whole new set of opportunities. 

Top 6 challenges that BFSI firms face

1. Severe talent crunch and too much data 

The talent shortage has been prevalent across all industries since the COVID-19 pandemic, and the BFSI industry is no exception. Employees evaluate their preferences and seek employers who offer better pay, and meaningful work, promote work-life balance, and have the aptitude for working remotely.  

On the other hand, at this juncture, banks need employees most to evaluate and analyze text documents. Moreover, the amount of digital information stored in databases is snowballing exponentially, with the majority of data being unstructured. The data stored in various formats such as text, video, audio, images, and other formats are challenging to analyze efficiently.  

BFSI organizations must analyze this unstructured data for a wide array of functions. Functions range from evaluating annual reports, earnings calls, and analysis briefings to deciding whether particular firms are a good investment. In addition, BFSI firms need to analyze contracts and loan agreements to assess potential risks, apart from conducting research for various other business reasons. They must also track their media and social media mentions and learn more about their competitors and promising business partners. 

How can NLP help here? 

NLP tools can highlight and flag keywords in reports and documents for further follow-up. This apart, NLP can summarize documents, filtering relevant information into small, readable portions that human employees can swiftly act on. 

2. Human error and bias 

Financial institutions struggle with human error and bias, leading to consequences for companies and customers. Though the industry tries to address the issue of bias by increasing diversity and inclusion, human beings often cannot overcome their inherent biases. One solution to this is automating the decision-making process. As the BFSI industry has already seen success with algorithmic trading and NLP automation in commercial loan applications, they can apply NLP in other areas such as personal loans, mortgages, employment applications, and more. The move can lead to gains in accuracy and efficiency. Primarily, this kind of activity enables the BFSI firms to fulfill their promise for the benefit of society, apart from improving and streamlining operations. 

3. Compliance with the growing regulations 

The BFSI industry is one of the topmost heavily regulated industries, with varying regulations depending on the location. NLP can assist financial firms in complying with these regulations in two key ways.  

• Firstly, by accelerating routine operations such as KYC (Know Your Customer) and anti-money laundering (AML) checks.  
• Secondly, it can provide structure to unstructured data, assisting compliance teams in identifying and mitigating risks. Thus, by accelerating compliance processes, NLP reduces operational costs and boosts customer satisfaction.  
• Additionally, NLP can assist firms in avoiding potential legal fines, fees, or damage to their reputation by ensuring complete compliance with their legal obligations. 

4. Fraud

Financial institutions faced an increase in fraudulent activity during the pandemic and after. According to LexisNexis, fraud costs for US financial services and lending firms have increased by 6.7% to 9.9%, resulting in a $4.00 loss for every $1 of fraud loss. The rise in mobile and online transactions has made it much easier for bots and identity thieves to commit fraud. On the other hand, the surge in housing prices has contributed to mortgage application fraud. NLP tools used for compliance can help firms detect fraudulent activity by identifying words, and phrases commonly used in fraud schemes, reducing resources needed for investigation and, at the same time, increasing accuracy. 

5. Human connection with customers 

According to Atos, online financial transactions now make up two-thirds of all financial transactions. While this has led to greater convenience and revenue growth for financial institutions, it has also resulted in a loss of trust from customers. A report shows that the proportion of consumers who have complete confidence in their bank to look after their long-term financial well-being dropped from 43% to 29% between 2018 and 2020. Though there are many reasons, not engaging in real conversations with customers has not helped bankers. However, customers still crave human interaction, especially after the pandemic. NLP tools can help financial institutions automate low-level text-related tasks, freeing time for more meaningful direct customer interactions. Ironically, the use of chatbots is also beneficial in increasing connection. 

6. Stiff competition from fintech 

In the BFSI sector, competition has become increasingly fierce due to the emergence of tech firms and fintech startups. Traditional financial institutions face tough competition from companies such as Chime, Alipay, Paytm, Razorpay, WeLab, Lufax, and others, who offer new options for transactions, saving, and investing. In this new digital marketplace, the barriers to entry are lower, and it just takes $50 million to set up a digital bank. Thus, banks need to become nimbler and more efficient to stay competitive. It is here that AI and NLP technologies can be helpful tools for achieving these goals. Hence, as part of their constant efforts to innovate and efficiently compete in the market, many BFSI organizations are investing in NLP. 

How can we help? 

Are you a CIO who is looking for solutions to specific challenges? At Saxon, our experts will help you with the perfect solution to address the obstacles and exponentially raise your business performance. Most of our solutions incorporate advanced NLP to help solve your enterprise needs.