Five key benefits global financial institutions can derive with the combination of AI-powered automation technologies

By Harpulak Bahadur, Founder and CEO,

Technology is helping drive innovation across a broad spectrum of sectors. When applied in the right way, the benefits that organizations can derive from it are manifold. Application of technologies like Artificial Intelligence (AI) and Natural Language Processing (NLP) can completely transform a sector. The McKinsey Global Institute, which surveyed over 2,300 participants from across industries, found that at least 5% of profits made by firms could be attributed to introducing AI in their businesses. It was also found that companies experienced a 20% increase in earnings thanks to AI.

A useful and profitable application of these technologies is in the banking and finance sector. Global banking institutions, which are spread across the world generate 2.5 quintillion bytes of data every day. That’s 10 raised to the power of 18! The information generated on a daily basis can be made to work for an organization by effectively applying technologies such as AI and NLP. The seamless intelligence to action is best understood through the likes of Facebook and Youtube etc., which have monetized this into trillion-dollar enterprises; wherein every data footprint is recorded, measured and evaluated.

Today, we can see FinTech firms like Revolut, Monzo, Klarna and others aggressively disrupting the traditional markets in which most global banks operate, and the pandemic has accelerated the situation. Banks today, are not just competing with other traditional financial institutions, but they have to tackle competition from virtual payment organizations like Paytm and Amazon Pay, online lending platforms like RateSetter and P2P lending firms like Zopa, which employ technology highly effectively. In such a scenario, it is imperative that global financial institutions leverage cutting edge tools to not just stay competitive but also improve their services.

Here’s looking at the five, best-case uses of AI and NLP traditional financial can derive to be ahead in the game:

1. Intelligent Insurance broking:
The use of AI and its benefits in speeding up processes related to the insurance industry are remarkable. As per Accenture’s Technology Vision for Insurance 2020 report, 81% of insurers have acknowledged that technology has become an unavoidable part of the human experience. Combining the power of a broker’s human experience and that of machine learning can not only give companies a huge advantage over their competitors but also help make better decisions.

With the help of AI, insurance brokers can analyze a ton of data and offer behaviour-based insurance along with saving time, reducing costs, improving customer experience and increasing their profitability along with reducing human errors and preventing frauds. According to a survey by Early Metrics, AI can save insurers $390 billion across their front, middle and back offices by 2030. The survey also revealed that startups in the sector raised $73.4 billion in 2020, despite the pandemic which shows that insurance firms are increasingly opting for intelligent insurance broking using AI to be ahead in the game.

2. Mortgage processing: The average consumer expects mortgage claims to get resolved as speedily as possible, however, going by the older methods this process can take up to 47 days. The current processes are lengthy and often times frustrating and require up to 40% of manual intervention. AI platforms offer a much quicker turnaround time and can clear claims the same day itself. Time consuming tasks involving documentation like gathering, reviewing and verifying can be speeded up and made less error-prone. By transforming the process into what resembles an assembly line, AI can help increase productivity and improve customer experience significantly.

Non-banking loan organizations like Banking Circle and iwoca among others now account for a major share of the market today. By easing the journey of the borrower, AI and ML can speed up the touchpoints like pre-approval, mortgage loan application, loan processing and underwriting. Employing processes like Intelligent Document Processing which extract data from complex, semi- and unstructured documents can help financial institutions become more productive, efficient and even help newer outfits to accelerate business scalability.

3. Claims processing: Claims processing is a critical function within an insurance company. According to data, the insurance industry in the US alone is set to lose $80 billion annually due to fraud. Traditionally, claims include several manual processes and claim information is submitted through multiple channels like emails, phones and is entered manually into the claims system. With claims managers entering data into multiple systems, the process is time consuming and prone to human error. By automating the process and using machine-learning to automatically extract and organize unstructured data, can help free employee time and enable them to service customers more effectively. According to a McKinsey survey, automating the claims process saves 34% of an employee’s data processing time.

Robotic Process Automation and AI can also be used to detect fraud in real-time, low risk claims can be processed quicker for immediate payouts and high-risk claims can be identified and sent for further reviews. As per Precedence Research, the global robotic process automation market size was valued at USD 2.65 billion in 2021. The insurance industry is aware of the benefits it can avail by employing these processes and the market is set to grow at a compound annual growth rate (CAGR) of 27.7% from 2021 to 2030.

4. Asset Management: When applied to Asset Management, AI and ML can help financial institutions real real-world benefits. The large amounts of data that Asset Managers have at hand can be used to help predict transactions, optimize portfolios and make highly accurate forecasts. An AI approach can extract information more efficiently from various sources of structured or unstructured data and generate accurate forecasts of bankruptcy and credit risk, macroeconomic trends, market volatility, financial crises much better than the traditional models.

The global asset management industry, faced with challenges including increasing data volumes, low interest rates and strict regulations have not just enabled, but made it essential for asset managers to apply AI-based solutions. The global AI industry size in asset management was valued at USD 990.4 million in 2019 and is expected to grow at a compound annual growth rate (CAGR) of 37.1% from 2020 to 2027. From improving customer experience to better investment decisions, AI and ML have emerged as the main priorities in the financial sector. Given the current competitive and volatile environment, asset managers can be helped to become better investors and minimize risk with the proper use of AI and not just increase profits but also provide a better investor experience.

5. Automated Middle office: Middle office serves a critical function in any financial institution. It manages risks, calculates the profits and losses and looks at the strength and functioning of the IT infrastructure. It tracks all the processes and deals made by the front office and reconciles them with the back office. Given the complexity of this part of the banking process and the fact that it generates a vast amount of data, AI and ML can be effectively utilized here to minimize risks and maintain a smooth functioning of a global financial institution. Revising strategies, improving user experience, mitigating risks and preventing fraud are the aspects that can be improved with the application of AI.

The research firm Autonomous Next in a recent survey found that bank around the world will be able to reduce costs by 22% by 2030 and incur savings of up to $1 trillion. Take for example the case of Regions Bank, which is considered among the largest full-service providers of consumer and commercial banking and wealth management in the United States of America. In a bid to build trusted AI solutions around helping reduce risk, detecting fraud and enhancing customer experiences, the bank managed to reduce fraud by 50%. It was also able to identify customers facing financial stress and enable the bank’s staff to identify their needs and help serve them better. This shows how deploying AI can help produce ethical, humane and trustworthy results that can drive decision-making in the middle office of a global financial institution.

As global financial institutions gear up to face the tough competition among themselves and from other middle-sized fintech companies, AI and ML are the tools that they have in their arsenal that can not just help them stay competitive but also achieve their growth targets and serve their customers effectively and quickly.

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