The Impact of AI in Fintech and the Financial Industry with SBIS and MyFinT

Impact of AI in Fintech and the Financial Industry

On the 25th of February 2022, our CEO See Wai Hun was invited by Sunway Business Investment Society (SBIS) and MYFinT: Malaysian Youth FinTech Association to shed insights on how Artificial Intelligence (AI) and Machine Learning (ML) are transforming Fintech and the financial industry.

Wai Hun began the session by introducing the Fintech landscape revolution and how the industry has evolved over time. Starting with Fintech 1.0, a phase that is characterised by the development of infrastructure to support globalised financial services; Fintech 2.0 marks the switch of the financial industry from analogue to digital. Meanwhile, Fintech 3.0 marks the era of “opening up” of the regulation to include numerous Fintech players to join the market. During the era of Fintech 3.0, the COVID-19 pandemic broke out and the world experienced a tectonic shift creating a convergence of the digital and physical spaces. Wai Hun shared how it took the e-commerce industry 10 years to grow from 6% to 16% in the total retail sales in the US, but just only eight weeks to grow from 16% to 27% during the pandemic

Challenges during Fintech 3.5

According to Wai Hun, we are now in the era of Fintech 3.5, where artificial intelligence and machine learning (AI/ML) plays a vital role in our lives. But before diving deeper into the aspects of Fintech 3.5, she first explained a few common challenges faced by most Fintechs.

Challenges in Fintech

Figure 1: Common challenges in the Fintech industry

1. Rising customer expectations

The first challenge that Wai Hun highlighted is that today’s consumer expects a high degree of personalisation and convenience in their banking experience. She also shared that if banks and financial institutions fail to meet customers’ expectations, they would risk being irrelevant in the future.

2. Inefficient data management

According to Wai Hun, businesses are struggling to separate valuable and irrelevant data, which leads to the lack of crucial insights that will help them make better business decisions. She also mentioned that the 2008 financial crisis was fueled by bad data that overstated how much derivatives such as mortgage-backed securities and collateralised debt obligations (CDO) were actually worth. Thus, key financial institutions such as Lehman Brothers went bankrupt after the subprime mortgages that formed these derivatives defaulted as their true value became apparent.

3. Inefficient credit risk management

Managing credit risk is also a challenge, especially in the early stages of debt collection. Conventional data sources used by banks and financial institutions may become obsolete overnight, and they regularly face difficulties in identifying self-curing and non-performing accounts which affects efficiency and productivity. 

4. Fraud threats

Lastly, Wai Hun mentioned that the increasing number of fraud cases such as phishing attacks and the Card Not Present (CNP) frauds also pose a challenge for the financial industry as the world continues to move towards digitalisation. Phishing scams are a huge threat to the financial industry, with one in every 99 emails being a phishing attack, and criminals are becoming more sophisticated with their schemes. 

So, how does AI and ML overcome these challenges?

According to Wai Hun, the biggest advantage of AI and ML is its ability to continuously learn and reinforce its learning through new data. Hence, both AI and ML can be utilised to overcome challenges in Fintechs and the financial industry.

AI Overcome Common Fintech Challenges

Figure 2: Utilising AI and ML to overcome the challenges

1. Improve customer experience

Banks are able to create a hyper-personalised customer journey, such as offering customised financial products or services by sending the right message to the right customer at the right time, by using AI and ML to analyse relevant data. To better illustrate her explanation, Wai Hun talked about Netflix’s recommendation engine that personalises movie recommendations based on users’ preferences so that users do not feel overwhelmed with a variety of options.

2. Predictive and prescriptive analytics for better business insights

Wai Hun continues by expounding how predictive and prescriptive analytics find and analyse patterns found in historical data like consumer behaviour can be used to predict customers’ future actions. For example, in the credit lending industry, traditional credit scoring systems make assumptions and test based on historical data to predict an applicant’s creditworthiness. But with AI and ML, banks and financial institutions will have a competitive advantage as both AI and ML provide a deeper understanding of consumers’ banking behaviour.

3. Credit/Risk Management to Reduce Revenue Loss

As conventional data may become obsolete overnight, Wai Hun shared that utilising AI and ML in credit risk management enables banks and financial institutions to predict the probability of an account defaulting. By leveraging on traditional and alternative data together with AI and ML, banks will be able to forecast self-curing customers and potential delinquents, and its prediction accuracy increases as well due to its self-learning ability.

4. Improve fraud detection, prediction and prevention

By using AI and ML, banks will be able to detect potential incidents of fraud and identity theft to a far more refined degree than ever before. As Wai Hun mentioned, AI enables systems to learn from each transaction, Hence, with machine learning, banks are able to train the AI to recognise and predict new behaviours in fraudulent transactions over time preventing customers from experiencing identity theft, as well as eliminating false declines.

So, why are we not seeing full AI adoption?

While AI may be the future of business, Wai Hun also mentioned that implementing AI can be quite challenging as AI is not easy to deploy. Furthermore, AI needs to be easy to use with minimal human involvement and lastly, it needs to be able to continuously learn from data so that it remains relevant. 

Enter Juris Mindcraft

Wai Hun offers our own proprietary AI platform, Juris Mindcraft as a solution to the challenges mentioned above. Juris Mindcraft is an AI platform that uses advanced machine learning techniques to build powerful AI models and make explainable recommendations. The AI platform aims to assist enterprises, especially banks and financial institutions in making intelligent business decisions and gaining insights to solve real-world problems. It is developed to remove the complexity of AI deployment, thus reducing the time taken to complete the cycle of building AI models to deploying the AI models from months to days!

“AI is helping companies accelerate at an unprecedented pace.” – See Wai Hun, CEO of JurisTech & iMoney.

The session ended with Wai Hun answering various questions from the audience related to digital transformation, AI, ML and the Fintech industry and followed by a heartwarming photo session. 

About JurisTech

JurisTech (Juris Technologies) is a leading Malaysian-based fintech company, specialising in enterprise-class software solutions for banks, financial institutions, and telecommunications companies in Malaysia, Southeast Asia, and beyond.

As one of the Fintech pioneers in Malaysia, our vision is to enable financial inclusion for the financial industry with our diverse range of solutions. Check out our latest AI-powered technology Juris Mindcraft, which helps banks and financial institutions to transform their digital landscape.

By | 2022-06-10T09:48:18+00:00 3rd March, 2022|Careers, News|

About the Author:

Sabrina Looi is a Marketing and Communications executive at JurisTech. She is highly interested to explore the diverse technologies in the financial services industry and enjoys keeping up-to-date with the latest market trends.