How NLP helps BFSI firms to overcome 6 of their topmost challenges - Saxon AI
AI and ML / April, 24 2023

 Unleashing the power of NLP in Finance: Overcoming the Top 6 Challenges in the BFSI sector  

Let us imagine a world where the banking and financial industries can overcome their most pressing challenges with ease, harnessing cutting-edge tech to revolutionize the way they operate. Welcome to the realm of Natural Language Processing (NLP), a game-changing subset of Artificial Intelligence that is revolutionizing the finance sector. From the exponential rise of chatbot interactions to the unprecedented cost savings of billions of dollars, NLP has truly captured the attention of industry leaders worldwide. In this blog, we unravel how NLP empowers financial institutions to navigate a rapidly evolving landscape and conquer their top challenges.

How the pandemic reshaped the finance 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. Before the pandemic, fintech organizations disrupted the financial services scenario with agile mobile applications. Although every finance organization’s scenario and business challenges may be exclusive, most consider AI or, precisely, NLP as a crucial tool they cannot afford to overlook. 

How does NLP support the finance sector?

 The finance sector grapples with a perpetual challenge- the overwhelming amount of excessive data and a shortage of skilled staff. The entire sector is all about numbers and data; making sense of all the data is even more critical than ever. But BFSI organizations need help to keep up with the data deluge. However, hope emerges in the form of Natural Language Processing, which is gaining prominence as a transformative solution.
With NLP, financial firms can tackle not only the routine customer service issues but also delve deeper, identify hidden biases, uncover fraudulent activities, and ensure unwavering compliance with stringent standards and yield competitive distinction. IDC projects that by 2025, NLP will take the lead in the estimated $120 billion annual investment in AI in the US. According to Markets & Markets, NLP will account for $35.1 billion in worldwide AI investments by 2026. We anticipate the finance sector to account for a significant portion of this spending.

What is NLP? 

In a nutshell, NLP 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. You can also check out our previous blog, Natural Language Processing (NLP) in AI, for a detailed understanding of NLP.

 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, NLP opens a whole new set of opportunities for finance firms. 

Top challenges that BFSI firms face and NLP helps overcome them:

Severe talent crunch and data overload

The finance industry is no stranger to the talent shortage that has plagued various sectors since the onset of the COVID-19 pandemic. Employees are now seeking employers who offer attractive compensation, meaningful work, work-life balance, and the flexibility to work remotely. However, this talent crunch coincides with a critical need for skilled professionals in finance institutions to evaluate and analyze an overwhelming amount of text documents.

Adding to the complexity is the explosion of digital information stored in databases, primarily unstructured data. Text, video, audio, images, and various other formats contribute to the challenge of efficiently analyzing this data. Within the finance industry, the analysis of unstructured data serves a wide range of functions, including evaluating annual reports, earnings calls, briefings analysis, and determining the suitability of potential investments. Additionally, finance organizations must scrutinize contracts and loan agreements to assess risks and conduct research on competitors and prospective business partners. Keeping track of media and social media mentions is also crucial in staying informed.

To overcome these hurdles, the finance industry seeks innovative solutions that can effectively harness the power of its vast data repositories. By leveraging advanced technologies, such as Natural Language Processing (NLP), finance firms can streamline the analysis of unstructured data, automate manual processes, and gain valuable insights to inform their decision-making. NLP offers the potential to address the talent crunch and empower finance professionals to navigate the information landscape more efficiently, driving more intelligent business outcomes and maintaining a competitive edge.

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. Among the recent developments, Generative AI (which involves NLP) can process a lot of data and make intelligent decisions and personalize the experience for the customer, adapting to new situations or tasks.

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 finance 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 fulfil their promise for the benefit of society, apart from improving and streamlining operations. 

Compliance with the growing regulations 

The finance 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 keyways.  

  • First, by accelerating routine operations such as KYC (Know Your Customer) and anti-money laundering (AML) checks.  
  • Secondly, it can structure 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. 


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 finance 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. 

Human connection with customers 

According to a recent study by the World Bank Group, 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. The onset of Generative AI can further provide better connections with customers and intelligent decision-making.

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, many finance organizations are investing in NLP as part of their constant efforts to innovate and efficiently compete in the market. 

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.

Get in Touch


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