The Role of AI & Machine Learning in Banking

In this blog post by Compare Banks, we cover AI/ML in banking.

Updated: May 18, 2024
Matt Crabtree

Written By

Matt Crabtree

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One distinctive feature of the financial technology sector over the last decade has been the emergence of challenger banks like Monzo and Starling.

Challenger banks are new financial institutions that strive to compete with more established financial institutions by providing consumers with more convenient and flexible services.

In light of the recent shift in focus to the customer and the resulting cooperative spirit among industry heavyweights, this month we take a look back at some of the defining characteristics of the challenger banks and their ongoing attempts to disrupt the status quo.

Artificial intelligence (AI) and machine learning (ML) are partway behind this; they’re being used to improve the banking experience for customers at both challenger and established financial institutions.

Artificial intelligence and machine learning have several uses, including fraud detection and prevention, customer journey simplification, and customer satisfaction guaranteeing.

We researched to major companies in the financial sector to get insight on the potential applications of AI/ML in banking. 

Role of AI and Machine Learning in Banking: What do the studies reveal?

To keep up with the ever-increasing competition in the banking industry and the ever-evolving demands of their customers, financial institutions today must adopt digital technologies. 

Artificial intelligence (AI) and machine learning (ML) have been a driving force behind many of the new digital technologies that have revolutionised banking in the modern era, from ATMs and online banking to voice recognition and chatbots, and from AI investment advisors to AI credit selectors.

One study found that front-office applications of AI include voice assistants and biometrics; back-office applications include credit underwriting with smart contracts infrastructure; and middle-office applications include anti-fraud risk monitoring and sophisticated legal and compliance procedures.

By using AI tools, financial institutions may save £300 billion by 2023. Almost 80% of U.S. financial institutions alone see the possibilities presented by AI. The development of artificial intelligence has really opened up several possibilities and difficulties. 

With the use of AI, banks have been able to streamline their sales processes and provide superior CRM tools for their clientele. Support for internal systems and procedures has become possible as a result of advancements in the automation of credit scoring, analyses, and awards.

Use of AI and Machine Learning in Banking: Top uses — Examples

What are the current most popular uses of AI and ML in banking? Let’s explore these examples.

1. Identifying and controlling risks

Automating credit risk testing helps financial institutions reduce risk because it produces reliable reports free of human errors. Further lowering the risk to the bank and the consumer is AI's main focus. Banks may benefit from AI's ability to foresee potential problems and take preventative measures by having the system analyse past risk instances. 

Risk evaluations now take just minutes instead of days or weeks because algorithms can process vast volumes of data in a fraction of the time it would take a person. Individuals with portfolios may benefit from big data's ability to better analyse risk and make informed investment choices.

This makes it likely that even more people will use mobile banking securely for banking in the years to come. 

2. Chatbots

Chatbots are undeniably one of the most cutting-edge instances of how AI is being put to use in the financial sector. Once deployed, they may continue operating around the clock, unlike humans who have set shifts. 

They also maintain gaining knowledge about a customer's unique consumption habits. It aids in effectively comprehending the needs of the consumer.

Banks may guarantee their availability to consumers at all hours by incorporating chatbots into banking applications, as well as robo-advice. Moreover, chatbots may provide personalised customer help and propose appropriate financial services and products by analysing user behaviour.

Erica, the Bank of America's virtual assistant, is a great example of an AI chatbot in banking applications. This intelligent chatbot can deal with things like paying off credit card debt and updating card security. In 2019, Erica handled more than 50 million customer queries.

3. Personalisation

Northern Trust's global head of banking and treasury services, Peter Sanchez, has some thoughts on the subject… 

Artificial intelligence and machine learning can allow personalised service, security, and regulatory compliance at scale and across borders, with efficiency that benefits our customers and the global company, for an established global bank with sophisticated institutional and affluent clientele.

Beginning with the individual customer, biometric access methods such as voice, face, and fingerprint identification may facilitate both convenience and safety.

In addition to improving predictions of account balances and expected transactions, AI data collecting and analysis of client behaviours may be used to recommend targeted product offers that are tailored to a customer's demands.

Fraud profiling employs machine learning to identify suspicious actions at the company, fund, or enterprise level. With the ability to swiftly analyse regulatory changes and discover consequences to terms and conditions across websites, mobile applications, and internal systems, AI is becoming an increasingly useful tool in the compliance industry.

4. Automation of critical processes

Meanwhile, Aki Eldar, CEO of Mirato, a TPRM platform, has some thoughts to share on business automation.

According to Aki, banks' growing reliance on third parties to deliver critical business processes and services exposes them to significant risk, which may come into key use when more controversial revolutions solidify, such as crypto in conventional banking.

Despite the fact that the global risk environment has gotten more complicated and contentious since new laws were enacted in 2013, TPRM programmes have not altered much.

Extreme events like natural disasters, pandemics, and huge cyberattacks are becoming more commonplace, and the present state of banks' TPRM programmes is not up to the task.

Subject matter experts still spend a lot of time on manual, labour-intensive procedures that are necessary for many TPRM methods, leaving little time for the more difficult job of preparing for and reacting to real risk occurrences.

By automating the human work, cutting-edge cognitive technologies like AI and machine learning are assisting banks in bolstering their TPRM programmes, allowing them to better detect and predict risk and promptly adapt to fast-developing regulatory requirements.

To evaluate and cross-reference third-party material with other data sources, AI and proprietary natural language processing (NLP) techniques may be used. Banks may avoid the time, effort, and money costs of manual TPRM labour thanks in large part to these solutions, which enhance process automation.

With the help of AI, information gleaned from surveys, supporting papers, data feeds, etc. may be converted into actionable risk exposure insights and plans of action.

The TPRM intelligence platform, driven by artificial intelligence, monitors and digitises data collecting from several sources around the clock, enabling financial institutions to make use of data sources that were previously unavailable or underutilised due to a lack of human resources.

Banks of the future will utilise AI to digitally change their operations, using the power of sophisticated data analytics to help create real benefits like as improved employee and customer experiences, simplified procedures, and mitigated exposure to third-party risk.

5. Conformity with regulations

The banking industry is one of the most strictly regulated in the world, from data privacy to the integrity of third-party accounting software. In order to prevent widespread bank failures, governments use their regulatory authorities to guarantee that banking clients are not utilising banks to commit financial crimes.

Most banks have a compliance department to handle issues like these, but doing so manually is time-consuming and expensive. Banks must continually update their procedures and operations to ensure compliance with the ever-evolving compliance rules.

Financial institutions may benefit from AI's usage of deep learning and natural language processing to decipher the latest compliance standards and make better decisions. Artificial intelligence in banking won't ever be able to replace the job of a compliance analyst, but it will help them do it more quickly and effectively.

6. Giving a hand to the “under-banked”

Director of the Milken Institute's Centre for Financial Markets and fintech expert Nicole Valentine agrees that artificial intelligence, together with machine learning, “are yet to realise their full and true power”.

She thinks it has the potential to be a game-changer in helping the many under- and unbanked families as well as providing small and medium-sized businesses with the financing they so desperately need.

In order to improve lending and credit choices that benefit customers, the banking industry has switched its attitude towards automation and machine learning, thanks in large part to AI and ML. We're not only interested in AI's capabilities; we want to know how many lives and companies it will change.

We need a new standard that uses inclusiveness as an integral part of any performance indicator in order to accurately measure progress and make certain that widespread access to financial services is accomplished.

There is a tremendous potential for traditional financial institutions, industry innovators, and financial regulators to collaborate in order to enhance economic and social mobility in underprivileged and traditionally excluded groups via the use of fintech.

7. Powerful data analysis

Payrailz's Vice President of AI Product Management Kavita Singh has remarked that AI and ML are useful for data analysis. 

Kavita argues that banks and other financial organisations have access to massive amounts of data that may be used to enhance the user experience. Artificial intelligence is tremendously helpful in the payments industry since it can assist account holders in managing their funds.

In order to assist account holders better manage their day-to-day money and alleviate financial stress, machine learning can examine their payment behaviours with regard to bills, spending, and saving. 

Artificial intelligence can recognise recurring behaviours, such as a banking customer needing a substantial sum of money on the 15th of each month to cover recurring expenses.

The AI may then calculate how much money will be needed to pay those expenses this month based on that trend and warn the user if they don't have enough. The list of potential outcomes is almost endless.

8. Credit assessment

“AI and Machine Learning are used differently in various parts of banking,” said Louis Brown, head of data science and advanced analytics at Chetwood Financial.

As a digital bank, Lous acknowledges that customers are increasingly curious about how AI and ML are being used to the credit department.

A growing number of innovative credit-related fields are beginning to make use of AI and Machine Learning for things like Open Banking, to widen access of banking to more people easier. Boosted decision trees are replacing more conventional ML methods such as Logistic Regression in applications such as credit scoring. 

To further explain ML's judgements, practitioners may resort to tools like SHAP (Shapley Additive exPlanations). There is also room for improvement in the way capital needs are calculated for credit risk based on internal ratings.

We anticipate that ML will continue to revolutionise the whole credit side of banking, with fraud protection continuing to be one of the top adopters.

Using Boosted Decision Trees and maybe Neural Networks, practitioners of Probability of Default Models may in the future witness significant improvements to their existing models. The most significant shift, however, will most likely occur in IRB Model Operations, where the use of ML methods might speed up the delivery of model updates.

9. Combating money laundering

Finally, the CEO of Sentinels, Joost van Houten, is certain that AI can aid in the fight against money laundering. 

He said that the instruments banks employ to identify and prevent fraud, money laundering, and the funding of terrorism have become inadequate, leading to an ever-increasing drain on bank resources and the immense growth of alternative offerings such as digital crypto wallets.

Less than a per cent of dirty money in the global financial system is ever confiscated, despite the European banking industry spending almost €100billion attempting to discover it. Obviously, something is wrong.

Financial institutions have come to realise that a more efficient approach to preventing money laundering and monitoring financial transactions is to use artificial intelligence (AI) and its subset, machine learning.

This technology has the potential to be tailored specifically to this problem. It can learn and adapt to new crime trends, allowing it to spot suspicious conduct and activity more quickly.

In order to detect and prevent fraud, challenger banks have begun using automated systems that are trained to monitor transactions.

When the system is given large, high-quality information (including customer relationships and the time of day a transaction occurred), it may detect actions, patterns, and associations that a human compliance officer would miss.

Sentinels believe that their method of uncovering illegal trends and typologies in transactions is novel since it focuses on the institution's customers and their counterparties (as the actors), be it traditional banking or on crypto exchanges.

By filtering out the transactions that pose no danger to a company's risk-based approach to the customer's profile and behaviour, their machine learning technology saves time and work for compliance teams.

In order to replace the vast quantities of (often false positive) rules-based warnings with accurate, risk-prioritized alerts, we focus on anomalies, the potentially problematic transactions.

While the use of AI and ML in regulatory compliance is still in its infancy, it is a promising field that must become pervasive if the banking sector is to stay up with the ever-evolving fraud threat.

Role of AI and machine learning in banking: The Verdict

It has been predicted that advances in AI and machine learning would completely alter the banking industry for the better. The technology has far-reaching consequences, yet most financial institutions are only beginning to embrace artificial intelligence. 

32% of financial services executives responded affirmatively to a study by Narrative Science and the National Business Research Institute on their use of artificial intelligence tools including predictive analytics, recommendation engines, and speech recognition. 

Legacy systems are a key barrier to the widespread use of AI. Leaders in the banking sector are hesitant to improve or alter their present technological procedures since banking is a more conventional business. Unfortunately, it's very uncommon for these older systems to get in the way of incorporating AI smoothly.

However, banks need to adopt technological solutions in order to compete with the growing number of financial technology (fintech) firms.

Customers want more from their financial institutions, and AI can assist. In addition, machine learning is an adaptable kind of AI that reduces banks' reliance on human specialists, freeing up staff to prioritise enhancing the customer service they provide.

See how to invest in artificial intelligence.

FAQs

How are banks using ML and AI to satisfy consumer needs?

As an industry, does banking have no choice but to accept the AI/ML future?

Use of AI in banking: what are examples?

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