At the same time, both the financial industry and regulators know that there are challenges and risks associated with AI that must be addressed.
Indeed, AI is now at an inflection point where it is primed to take a leap forward. The financial institutions that have the right infrastructure, culture, and mindset that allow them to make full use of the technology will gain important competitive advantages in an increasingly digitized market environment.
The state of AI in Asia’s financial services sector
There are a number of areas where AI is being utilized by the financial industry in the APAC region. One of the most important is customer service. AI chatbots and virtual assistants can automatically answer basic questions about checking account balances or booking branch appointments. The purpose of these AI tools idea is to minimize friction for customers and costs for banks.
Most banks across Asia already have a version of their own chatbot, either white-labelled or built in-house. Malaysian bank CIMB, for example, introduced the first conversational style and real-time chatbot for commercial banking which was the first in-market chatbot at the time of release.
Meanwhile, AI-powered robo-advisors increasingly provide personalized investment advice to retail investors. Many traditional financial institutions have launched robo-advisory platforms, and there has also been a proliferation of fintech robo-advisors across Asia. The latter include startups such as Endowus, Syfe, Stashaway, and Robowealth. This trend is likely to continue as more investors seek low-cost, digital options.
AI-based systems can also crunch vast data troves to assess creditworthiness and make lending decisions, improving the efficiency of the lending process while reducing default risks. AI can capture insights from alternative sources of data which then makes it possible to extend loans to individuals who do not have any credit history. This is especially pertinent in Southeast Asia, where 60% of MSMEs surveyed by Tech for Good Institute in 2021 were unable to get a loan when they needed financing. UnionBank in the Philippines, for example, has utilized AI- powered credit scoring models to generate credit scores for the unbanked through the use of such alternative data.
Additionally, AI-powered systems can detect patterns of fraudulent activity and money laundering that would be difficult for humans to spot. This is especially important as financial crime continues to evolve and become more sophisticated. For its part, Singapore’s DBS is using AI to reduce the number of false positives as well as prioritize alerts such that analysts can dedicate more time to higher risk activities. The bank also utilizes AI programs to gather massive amounts of bank data needed to make decisions on alerts.
Distinguishing reality from hype
To a certain extent, the AI hype bubble has had a detrimental effect on the technology’s real-world applications. AI investors, founders of AI startups and some consultants have a vested interest in exaggerating the technology’s importance for financial reasons. How many times have we heard that AI is coming for our jobs? Or that it will save companies mammoth sums? Or that it will change everything?
Yet while we expected conversational AI to reduce reliance on call centers, chatbots are still not able to carry out full conversations and in some cases are still scenario based, only able to return a pre-determined set of replies to a limited set of scenarios. If queries from customers are outside of the set, customers will be directed to a call/chat center.
In addition, financial services are heavily regulated. Firms in the industry must comply with a wide range of regulations, which can make it difficult to implement new technologies like AI. Financial institutions must have a strong understanding of how they use AI to ensure customer satisfaction, optimal business performance and regulatory compliance.
Financial firms should understand algorithms powering AI tools that combat money laundering, especially when it concerns the use of customer data. There are concerns about the potential ethical implications of using AI in financial decision-making, such as bias and discrimination.
Singapore has, as a result of such concerns, launched the world’s first AI Governance Testing Framework and Toolkit. A.I. Verify aims to promote transparency and ethical use of AI between companies and their stakeholders through a combination of technical tests and process checks.
We can expect more countries in Asia to follow Singapore’s lead. Financial institutions must demonstrate the trustworthiness and transparency of AI systems to both regulators and customers. Instead of just deploying AI, banks will increasingly need to allocate more resources to hiring the right talent to ensure customer data is handled and stored properly.
That said, overall, AI is already having a significant impact on the financial services industry, and this trend is expected to continue as the technology matures and becomes more widely available. AI usage in financial services is becoming the rule, not the exception.
The incorporation of AI in financial services will bring many benefits such as cost reduction, improved efficiency, better customer service and more accurate decision-making. At the same time, the financial industry is also aware that there are challenges and risks associated with AI, such as data privacy, security, job displacement, and ethical concerns, that must be addressed.
In the years ahead, AI adoption in finance will steadily accelerate in a wide range of applications, from fraud detection and risk management to personal finance and financial advice.
The financial institutions that maximize AI’s potential will be those who successfully balance business benefits against regulatory complexity and the need to maintain customers’ trust.