Can AI & Fintech forever change the financial landscape?


The global computational arms race has augmented the technologies far and wide, bolstering their capabilities, and opening up a plethora of possibilities for companies across industries.

FinTech is no exception to this, specifically when narrowing our focus toward artificial intelligence (AI). 

Global AI in the Fintech market was estimated at USD 6.67 billion in 2019 and is expected to reach USD 22.60 billion by 2025; the market is also expected to witness a CAGR of 23.37 percent over the next five years, according to a report by Mordor Intelligence.

When looking to the financial sector, AI is used to examine cash, credit, and investment accounts in search of a person’s overall financial health, while keeping up with real time changes and then creating tailored advice based on the data. 

Not only that, but AI coupled with Machine Learning (ML) are revolutionizing the sector by forging more efficient processes, better financial analysis, customer engagement, and fraud prevention. 

This lays the ground for financial institutions to reduce operational costs by 22 percent by 2030, according to a report by Autonomous Research. 

What do AI & ML bring to the table? 

Fintech is still considered as a developing and newly emerging market, which is why it requires industry-specific solutions to meet its goals. These solutions and technologies offer higher accuracy levels, greatly reducing human errors while speeding up the financial process across the board. 

“AI-based financial solutions are focused on the crucial needs of the modern financial sector such as better customer experience, cost-effectiveness, real time data integration, and enhanced security. Adoption of AI and its allied applications enables the industry to create a better, engaging financial environment for its customers,” the report by Autonomous Research highlighted. 

AI is further on its way to becoming mainstream in Financial Services within the short-term. For instance, according to a survey conducted by Cambridge Centre for Alternative Finance (CCAF) in 2020, 85 percent of all respondents in the study used some forms of AI, with the fintech companies being slightly ahead of incumbents in the adoption of AI.

In parallel, banks are adopting AI solutions to leverage information and insights locked away in unstructured documents and automate the manual process done traditionally by banks in double-quick time.

An example of this can be seen through the efforts made by Temenos, a Swiss-based banking software company, who announced the launch of eight propositions – using innovative Explainable AI (XAI) and cloud technologies to help banks and financial institutions in their immediate response to the COVID-19 crisis.

According to a study by research group Forrester, around 50 percent of financial services and insurance companies already use AI globally. 

And the number is expected to grow with newer technology advancements.

In addition, it is further evolving into cognitive process automation, where AI systems can perform even more complex automation processes.

There is a myriad of ways that AI can help boost and reinforce financial institutions across the board while heating up competition between traditional banks, big tech companies attempting to penetrate the industry, and Fintech startups. 

1. Automated customer engagement

As customers become more knowledgeable about products and services, while being showered with different options and solutions they find online, competition has become fierce; the need for better, safer, and tailor-made solutions is increasing as companies attempt to attract as many customers as possible. 

And Automation is delivering just that, especially in terms of customer service.

People are already slowly seeing them surface with the emergence of AI interfaces and Chatbots; these solutions are not only getting smarter and becoming harder to differentiate from real human beings but are also drastically bringing down the cost of staffing.

AI is already starting to make headway by automating back-office processes rendering it as seamless as possible. 

In the financial sector specifically, time is money and reducing timeframes can prove to be a key aspect especially for startups starting their journeys on bootstrapped budgets. 

2. Sharp decision-making

Since AI champions data management, it allows decision makers to accurately take the best decision in each circumstance encountered. In parallel, ML can effectively analyze major stacks of data to deliver the necessary outcomes to determine outliers.

This also reflects on a company’s ability to narrow down their focus on more specific problems that arise.

For instance, in May 2020, Traydstream, a UK-based Fintech platform that scans trade documents with artificial Intelligence (AI), partnered with Infosys Finacle to implement blockchain technology and further automate trade finance. 

The partnership will allow Finacle’s blockchain tech, called Finacle TradeConnect, to be integrated with Traydstream’s platform, which uses AI to scan documents and cut down the time it takes to check on rules or regulations in trade, where mistakes can be costly and time-consuming to correct.

3. Fraud prevention

AI has the potential to lead the fight against cybercrimes, especially when it comes to uncovering fraudulent transactions. 

According to U.S.-based AI research and advisory company, Emerj, AI solutions for fraud detection are trained to have a baseline sense of normalcy for the contents of banking transactions, loan applications, or information for opening a new account.

The software can then notify a human monitor of any deviations from the normal pattern so that they may review it. The monitor can accept or reject this alert, which signals to the machine learning model that its determination of fraud from a transaction, application, or customer information is correct or not.

4. Trading and wealth management

AI and ML have struck the sweet spot for wealth advisors at large firms since not only does it offer to reduce costs and time but allows for the monitoring and tracking of regulatory risks. 

“AI software can today provide wealth managers insights and recommendations by taking into account a client’s preferences, financial trading trends, and regulations associated with advisory services at a speed and scale that cannot be matched by human analysts,” the report by Emerj highlighted. 

In parallel, AI can help traders make accurate decisions that hold the fate of millions of dollars, such as managing asset portfolios and their diversification, processing market data and stock market indices for their application in technical analysis, building behavioral models during market turmoil, and identifying cases of collusion and manipulation in the market.

Risk profiles

As brick and mortar retailers continue to face challenges due to the onset of COVID-19 pandemic, many merchants are implementing point-of-sale financing alternatives as a potential new avenue for growth. 

“These tools showcase excellent capabilities in automating the process of profiling clients based on their risk profile; profiling work helps experts give product recommendations to customers in an appropriate and automated way to exactly fit their needs,” the report by Mordor Intelligence noted.

Apart from utilizing current data like bank account statements for underwriting, these players are further using AI models to assess consumer behaviors based on their transaction history, product purchase, and other data points to create a sharper customer risk profile.

Predictive analytics

Banks, fintech firms and credit card companies are trying to leverage the power of predictive analytics and merging it with their fraud detection procedures to better flag false positives.

For instance, in May 2020, SparkCognition, an industrial AI company, announced that the Japanese AI and fintech company, MILIZE Co. Ltd, would offer financial institutions fraud detection and anti-money laundering (AML) solutions. 

These solutions are built using the automated machine learning software of SparkCognition. As a result, the software detects fraudulent transactions with about 90 percent accuracy, which is anticipated to lead to a significant improvement in the credit card companies’ profitability

With the slow emergence and rollout of 5G on the horizon, Fintech and AI will drastically grow to become dependent on each other to make things easier internally, while providing customers with seamless experiences and faster transactions.

Although the industry is still experimenting with the tech’s capabilities, disruptive and innovative solutions will stay afloat, making artificial intelligence vital for the financial sector as a whole.