AI’s role in banking: benefits and risks (2024)

23 Nov 2023

Although we think ofAIas something groundbreaking,AI’s role in bankingand financial services has been transformative since its inception.

The implementation ofAI in bankingapplications and software solutions has significantly revolutionized the way companies access and manage their finances. Itreduces costs,increases productivity, and aids in decision-making based on information that would otherwise be incomprehensible to any human being.

Not only that, intelligent algorithms are capable of detectingfraudulent informationin a matter of seconds , makingAI’s role in bankingessential in the fight againstfraud.

Already in 2020,Business Insidersuggested that nearly 80% of banks were aware of theimpact of AI in banking. Three years later, this potential has exploded, andAIis already part of everyday life in thebanking sector.

The reason behind this rapid prominence ofAI in bankingis due to the amount of data that must be used. Banks have all this data, and therefore, are able to useAIto exceed human capabilities andpredict outcomes.

When alarge number of variables are involved, which is common in this sector,artificial intelligenceplays a crucial role, benefiting the business in many ways.

Benefits of AI in banking

The revolutionary impact ofAI in bankingis already a fact. Automating processes, improving the customer experience, mitigating risks, boosting efficiency, and transforming overall operations.

However, the applications ofAI in bankinghave not yet reached all their capabilities, and these are just some of thebenefitsit can bring.

Its future possibilities? They seem, to this day, infinite. As the implementation ofAIcontinues to evolve, it is expected toredefine banking operationsin better ways in the coming years.

Improvement in fraud detection

AIcan analyzelarge amounts of datato detectfraudulent transactionsmore efficiently than humans.Machine learning algorithmscan learn from past fraud cases to identifypatternsandanomaliesthat can be used toprevent future fraud.

Millions of transactions occur daily. Users pay bills, withdraw money, deposit checks, and do much more through apps or online accounts. Therefore, there is a growing need for thebanking sectorto step up its efforts infraud detection.

This is whereartificial intelligence in bankingcomes into play.AIandmachine learninghelp banks identify fraudulent activities, track faults in their systems,minimize risks, and improve overall online finance security.

AIcan also help banks handlecyber threats. In 2019, thefinancial sectoraccounted for29% of all cyber attacks, making it the most attacked industry.

For example, solutions that uses AI likeTransmit Security’sdetection and response, are a huge help preventing identity fraud, even after a successful phishing attack.

The platform continuously collects information of dozens of parameters, including device fingerprinting, behavioral biometrics,bot detection,network analysis, authentication strength and app activity patterns. Those are cross-correlated with global threat intelligence,fraud patternsand the individual user’s typical behavior, so, as commented, even with the right user credentials theft after a cyberattack, it can be detected and fraud stopped.

This a just an example of how, with thecontinuous monitoring capabilitiesofartificial intelligencein financial services, banks can respond to potentialcyber attacksbefore they affect employees, customers, or internal systems.

Personalized customer experiences

AIcan help banks providepersonalized experiencesto their customers.

With the aid of data analysis,bankscan understand theircustomers’ preferences,financial behaviorto offer personalized financial solutions. This can lead to greatercustomer satisfactionand loyalty.

In terms ofcustomer service,chatbotsare one of the best examples of practical applications ofAI in banking. Once deployed, they work 24/7,allowing humans to use their time more efficientlyon inquiries that require personalized attention.

An example of a chatbot for banking applications isAmerican Expressand itsAmEx chatbot.

American Expresscredit card holders can link their cards to theAmEx chatboton Messenger to receive personalized recommendations and notifications.

Contextual recommendations,reminders about exclusive card benefits, etc., are examples of such practices based on its learning.

Operational efficiency

Automation of routine tasks and optimization of operations are two of the key factors in which the role of AI in banking is crucial. Automating processes leads to not only a significant cost savings, but also greater operational efficiency.

WithAI in banking, organizations can efficiently automate a wide range of routine tasks such as data entry, account reconciliation, document processing, etc.

It also allows the bank to speed up certain online processes by offeringreal-time services, since some documents are now not processed manually by humans but by AI, which improves the quality of the final service.

Currently,VASSis usingartificial intelligence to improve processes, including generating test cases for code.

This type of automation not only frees up human resources and allows them to focus on more essential tasks, but also reduces the risk of errors and speeds up the completion of processes.

Risk management

External global factors such ascurrency fluctuations,natural disasters, orpolitical instabilityseverely impact thefinancial sector. During these times of volatility, it is crucial to make business decisions with great caution.

AI in bankingis capable of performingpredictive analysisthat provides a reasonably clear picture of what is to come, helping the sector to be prepared and to make decisions in a timely manner.

AsAIis capable of analyzing large amounts of information, its algorithms can identify patterns of behaviour and transpose them as risk predictors.

This process allows for a reduction in the time and resources needed, as well as offeringloansthat aresafer for both parties.

Undoubtedly,AIis an excellent tool to assist in decision-making, but we must not forget that the final word in all processes should come from the hands of experts.

Risks and challenges of AI in banking

While we have observed numerous advantages of applyingAI in banking, as with any technology that advances and changes in a matter of seconds, we must not lose sight of therisksthat its use implies.

There are greatchallengesto be faced in order to useartificial intelligencein a sensible and ethical way.

Data Privacy and Security

Bankscollect large amounts of data from customers, andAI algorithmsrequire access to this data to function effectively.

Ifsensitive dataexist, as is often the case withfinancial data, anysecurity breachcould have serious consequences. Additionally, there is the issue of compliance withprivacy regulations, such as the General Data Protection Regulation (GDPR) of theEuropean Union.

In fact, theEUhas published adraft lawto regulateAI, which details when the artificial intelligence can and cannot be used.

Having specific regulations to oversee the use ofAIis vital to ensuredata privacyandsecurity, in order topreventdata breachesand protectcustomers’ confidential information.

Moreover,AI modelsneed to be constantly validated and monitored to ensure they are working as expected.

An example of this is the use of the “HIL”,Human in the Looptechnique, which combines humans and machines through the iteration and feedback from both parties.

AsAI modelsdon’t always predict with full confidence, humans play a crucial role in providing feedback. They are capable of training and refining the algorithm, improving it over time.

Undoubtedly, it means a great challenge, given the dynamic and complex nature ofAI algorithms.

Ethical considerations

The use ofAI in bankingraisesethical concerns, such asbias in decision makingand discrimination.

AI algorithmscan be biased if the data they were trained with are biased. This could lead to unfair ordiscriminatory decisions, such as loan denials based on demographic factors.

Banksmust ensure that theirAI systemsaretransparent,fair, andimpartial. Alert systems can be used to flag unusual or unexpected actions to employees for further analysis.

As said before, withhuman in the loop processes, decisions that are made by theAIwill be executed only after they have been approved by a human. Both of these systems have limitations, but they are key tools for risk mitigation.

Customer centered work

Aside from complying withgovernmental and sector-specific regulations, banking must always work in thecustomer’s interest.

For this reason,banking teamsmust work with a full understanding of howAIand its integration into the customer’s daily life is affecting them.

Not having full knowledge about this factor could put the customer at risk, and theloss of credibilityfor the entity would be enormous.

Relying solely on a third party, such asAI, leaves companies in situations where they are unable to explain howartificial intelligence modelsmake decisions.

For this reason,risk and compliance teamsare especially important in the process of ensuring thereliability of processes that involve the use of AI.

AI’s role in bankingpresents exciting opportunities, but it also poses significant challenges.

Banksmust carefully consider the ethical, regulatory, andsecurity implicationsof adoptingAIto ensure they leverage the technology effectively and responsibly.

In doing so,bankscan providebetter customer experiences,optimize operations, andmanage riskmore effectively.

The key to a successful adoption ofAI in bankingis to find the right balance betweeninnovationandrisk management.

VASS is currently working withMachine Learning(ML),Incremental LearningandNatural Language Processing(NLP) to help clients to avoid, among other things, false positives and strengthen their own AML (Anti-Money Laundering) policies.

Natural Language Processing(NLP) permits to analyze and understand the textual information associated with transactions, such as reference notes or descriptions. This can provide more context andhelp identify deceptive transactions.

ThroughIncremental Learning, as new transactions are received, the model adapts nearly in real-time to prevent its obsolescence.

Thanks toMachine Learningfor Behaviour Modeling, it is possible to analyze transaction history and buildbehaviour profilesof customers and suppliers. It allows the identification of normal activity patterns and the detection of anomalies that might indicate fraudulent activities or identify false positives.

Theartificial intelligencehas transformed the banking landscape, offering personalized, efficient, andreal-time solutionsthat enhance the customer experience and optimize internal operations. However, it is not without challenges.

Data privacy management, ethics in automated decision-making, and the potential to perpetuate pre-existing biases are areas that require careful scrutiny and regulation.

Banking, as a crucial industry in people’s daily lives, must address these risks with transparency and responsibility.

Adoptingartificial intelligence in bankingis not just a matter of technological innovation, but also of trust and ethics. By properly balancing thebenefits and risks of AI, the banking sector can lead the way to a more efficient and inclusive future, where humans and machines work together.

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