The global AI market was valued at 62.35 billion in 2020, and the market is expected to expand with a CAGR of 40.2% between 2021 to 2028. The banking and financial sector accounts for 20-25% of the global economy. It is unlikely that a market as big as banking and finance would not catch up on a trend as widespread and revolutionary as AI. In fact, even before the pandemic ushered in an era of technological revolution, the banking sector had started adopting AI for both front and back-office tasks.
So, what (and how much) are the benefits of using AI for banks? What does the market look like in 2022? What do the experts see becoming a reality in the years to come?
Find answers to all the questions right here.
Artificial Intelligence in the Banking World: Going by the Numbers
Before we move any further, let’s take a look at what the numbers have to say about the use and impact of artificial intelligence in the banking sector
A McKinsey report suggested that by using AI, the banking sector can earn an additional $1 trillion dollar in value.
With the application of AI, banks can save an estimated $447 billion by 2023. Out of that, $416 billion of savings will come from AI use in the front and middle office.
A whopping 80% of banks in an OpenText survey of financial survey professionals said they were highly aware of the potential benefits of AI.
75% of banks with over $100 billion in assets have already begun implementing AI strategies. For banks with less than $100 billion in assets, the percentage was 46%.
Joint research by the National Business Research Institute and Narrative Science in 2020 concluded that 32% of banks have started leveraging AI technologies like predictive analytics and voice recognition to get a competitive edge in the market.
Benefits of AI in Banking
The numbers make it clear that AI is gaining traction in the banking world. The banking industry’s fascination with artificial intelligence is not just because AI is in-vouge. The primary benefits of AI in banking include:
Better service response
Elimination of human errors and biases
Greater scope for personalization
Enhancement in customer trust and satisfaction
Facilitation of the concept of banking-from-home
Due to these benefits, stakeholders are exploring and experimenting with more innovative and newer ways of leveraging artificial intelligence, Big Data, and machine learning for banks.
Top Applications of AI in Banking
Artificial intelligence has potentially limitless use cases, in general, and even if we specifically talk about the banking sector. Optimist forecasters dream of days when AI would completely take over the banking world and our entire banking system would be run by these intelligent machines.
While that is still a far-fetched dream, here are 5 applications of AI in banking that we can see in action in 2022.
1. AI Cybersecurity Against Financial Fraud
In 2020, over 290,000 cybersecurity issues were reported by the banking sector. That makes it important for banks to take not just responsive but proactive measures. They need to nip cyber security vulnerabilities in the bud and protect employees and customers from financial fraud, and AI is helping with that.
Denmark’s largest bank, Danske Bank, has replaced its old rule-based fraud detection system with an AI-powered algorithm. The deep learning tool now helps the bank cut down the risk of financial fraud by 50%. The solution also reduced false positives by 60% resulting in less frequent false alarms.
Also, Amazon recently purchased an AI cyber security startup harvest.AI. This further solidifies the fact that the use of AI in cyber security and financial fraud prevention has serious potential.
2. AI-Powered Chatbots for Seamless Customer Interaction
Chatbots are one of the most-used applications of artificial intelligence, not only in banking but across the spectrum. Once, AI chatbots can work 24/7 to be available for customers. In fact, in several surveys and market research studies, it has been found that people actually prefer interacting with bots instead of humans. This can be attributed to the use of natural language processing for AI chatbots. With NLP, AI chatbots are better able to understand user queries and communicate in a seemingly humane way.
An example of AI chatbots in banking can be seen in the Bank of America with Erica, the virtual assistant. Erica handled 50 million client requests in 2019 and can handle requests including card security updates and credit card debt reduction.
3. Personalized Banking for Higher Customer Retention
Digital-savvy banking customers today need more than what traditional banking can offer. With AI, banks can deliver the personalized solutions that customers are seeking.
An Accenture survey suggested that 54% of banking customers wanted an automated tool to help monitor budgets and suggest real-time spending adjustments. AI can make that, and a lot more, possible.
Now one might wonder if customers would be willing to take advice from a bot? Well, 44% of people said they are “very willing” to accept computer-generated banking advice. Thus, this AI use case in banking is actionable with decent acceptance levels at present.
Practical application of AI-powered personalized banking can be seen at the TD Bank Group. They have made public their plans to integrate Kasisto’s AI technology into their mobile app. The solution would give customers real-time support and insights into their spending patterns.
4. Transparent Loan and Credit Decisions With Artificial Intelligence
Most banks are still relying on credit scores, credit history, and references to ascertain a prospect’s creditworthiness. This process is not just paintaking and time-consuming but also not transparent. With the use of AI in making loan and credit decisions, banks can reduce manual grunt and increase transparency. Also, with data-backed insights offered by AI solutions, banks can cut losses and make more profitable decisions.
While examples of uses of AI in the banking industry for such decision-making are not many, some banks are now using AI to find creditworthiness reports about people with limited credit histories. Also, such systems can alert banks about potentially risky spending behavior and patterns of their clients.
5. AI Ensuring Ethical Frameworks
Ethical considerations are becoming more prevalent across the board, especially in the financial world. This is because customers are becoming more aware and are taking charge of how they want their data to be used.
Artificial intelligence can greatly help banks develop ethical frameworks for data processing and build customer trust.
HSBC can be seen as a market leader in this sphere. HSBC is the first financial services company to have created an AI and data ethics principle. They have also partnered with the Monetary Authority of Singapore and the Alan Turing Institute to develop a framework for the ethical adoption of AI in banking.
Challenges That Need To Be Tackled in 2022
While the benefits and use cases of artificial intelligence in banking are plenty, the path ahead is not without its fair share of challenges. The key challenges pleading the AI niche in banking include:
Customers and employees in tier II and tier III cities across the globe are showing unwillingness to adapt to AI-enhanced methods. The initial inertia against moving away from conventional practices needs to be overcome.
There seems to be a disconnect between what the customers want banks to offer and the solutions banks put in place. Proper data and marketing understanding is required to bridge this gap.
Regulatory requirements and compliance pressures are providing to be a limiting factor for the adoption of AI by banks. For example, net banking and online transactions come under the ambit of privacy regulation and thus, compliance becomes inevitable.
The workforce of the banking sector is not yet skilled enough to work with advanced AI tools and software. Upskilling efforts need to be taken by banks.
With that, we can conclude that the future of AI in banking looks promising and 2022 could be an inflection point where banks stop playing around with AI and experimental efforts transform into something that can yield tangible results.