AI in Finance: Applications, Examples & Benefits

Existing regulatory and supervisory requirements may need to be clarified and sometimes adjusted to address some of the perceived incompatibilities of existing arrangements with AI applications. Interestingly, AI applications risk being held to a higher standard and thus subjected to a more onerous explainability requirement as compared to other technologies or complex mathematical models in finance, with negative repercussions for innovation (Hardoon, 2020[33]). The objective of the explainability analysis at committee level should focus on the underlying risks that the model might be exposing the firm to, and whether these are manageable, instead of its underlying mathematical promise. A minimum level of explainability would still need to be ensured for a model committee to be able to analyse the model brought to the committee and be comfortable with its deployment. Synthetic datasets can also allow financial firms to secure non-disclosive computation to protect consumer privacy, another of the important challenges of data use in AI, by creating anonymous datasets that comply with privacy requirements.

  • It’s equipped with generative AI to enhance productivity by aiding users in drafting documents, revising content and conducting research.
  • By deploying accurate algorithms and predictive models, financial institutions can automate their operations and gain valuable insights into customer behavior.
  • Researchers suggest that, in the future, AI could also be integrated for forecasting and automating in ‘self-learned’ smart contracts, similar to models applying reinforcement learning AI techniques (Almasoud et al., 2020[27]).
  • There are also specific features based on portfolio specifics — for example, organizations using the platform for loan management can expect lender reporting, lender approvals and configurable dashboards.
  • An f5 case study provides an overview of how one bank used its solutions to enhance security and resilience, while mitigating key cybersecurity threats.

While this may seem like an area where machines shouldn’t be involved, the advantages of artificial intelligence applications are significant. Expense fraud is a pervasive problem that continues to plague companies of all sizes and industries. In fact, a recent survey by the Association of Certified Fraud Examiners found that organizations lose an estimated 5% of their revenue to fraud each year, with expense reimbursement fraud being one of the most common types of fraud. For example, with Yokoy, detecting duplicate payments is fully automated and is a matter of seconds, no human input being required.

Algorithmic trading

AI techniques such as NLP12 are already being tested for use in the analysis of patterns in smart contract execution so as to detect fraudulent activity and enhance the security of the network. Importantly, AI can test the code in ways that human code reviewers cannot, both in terms of speed and in terms of level of detail. Given that code is the underlying basis of any smart contract, flawless coding is fundamental for the robustness of smart contracts.

Access to customer data by firms that fall outside the regulatory perimeter, such as BigTech, raises risks of concentrations and dependencies on a few large players. Unequal access to data and potential dominance in the sourcing of big data by few big BigTech in particular, could reduce the capacity of smaller players to compete in the market for AI-based products/services. AI is being used by banks and fintech lenders in a variety of back-office and client-facing use-cases. Chat-bots powered by AI are deployed in client on-boarding and customer service, AI techniques are used for KYC, AML/CFT checks, ML models help recognise abnormal transactions and identify suspicious and/or fraudulent activity, while AI is also used for risk management purposes. When it comes to credit risk management of loan portfolios, ML models used to predict corporate defaults have been shown to produce superior results compared to standard statistical models (e.g. logic regressions) when limited information is available (Bank of Italy, 2019[17]). AI-based systems can also help analyse the degree of interconnectedness between borrowers, allowing for better risk management of lending portfolios.

2.3. Credit intermediation and assessment of creditworthiness

Learn how to transform your essential finance processes with trusted data, AI-insights and automation. Overall, the integration of AI in finance is creating a new era of data-driven decision-making, efficiency, security and customer experience in the financial sector. AI can help companies drive accountability transparency and meet their governance and regulatory obligations. For example, financial institutions want to be able to weed out implicit book balance bias and uncertainty in applying the power of AI to fight money laundering and other financial crimes. Finally, companies are deploying AI-guided digital assistants that make it easier to find information and get work done, no matter where you are. For example, finance organizations can leverage digital assistants to notify teams when expenses are out of compliance or to automatically submit expense reports for faster reimbursement.


A number of defences are available to traders wishing to mitigate some of the unintended consequences of AI-driven algorithmic trading, such as automated control mechanisms, referred to as ‘kill switches’. These mechanisms are the ultimate line of defence of traders, and instantly switch off the model and replace technology with human handling when the algorithm goes beyond the risk system and do not behave in accordance with the intended purpose. In Canada, for instance, firms are required to have built-in ‘override’ functionalities that automatically disengage the operation of the system or allows the firm to do so remotely, should need be (IIROC, 2012[14]). Unlike automation software that can do simple, rote tasks, artificial intelligence performs tasks that historically could only be handled by humans.

Simplify your spend management

Here are a few examples of companies using AI and blockchain to raise capital, manage crypto and more. Bank One implemented Darktace’s Antigena Email solution to stop impersonation and malware attacks, according to a case study. The bank saw a rapid decrease in email attacks and has since used additional Darktrace solutions across its business. Having good credit makes it easier to access favorable financing options, land jobs and rent apartments. So many of life’s necessities hinge on credit history, which makes the approval process for loans and cards important.

And as AI technology continues to advance and become more accessible, it’s expected that more finance departments will adopt it. In fact, it’s likely that most of the processes that can be automated with machine learning and AI will be. The use of finance AI is on the rise, a study by Gartner estimating that by 2025, 75% of finance teams will be using AI-powered applications to automate tasks and improve decision-making processes. Generative Al’s large language models applied to the financial realm marks a significant leap forward. With generative AI for finance at the forefront, this new AI technology guides the path towards strategic integration while addressing the accompanying challenges, ultimately driving transformative growth.

Customer service

AI-driven systems may exacerbate illegal practices aiming to manipulate the markets, such as ‘spoofing’6, by making it more difficult for supervisors to identify such practices if collusion among machines is in place. The possible simultaneous execution of large sales or purchases by traders using the similar AI-based models could give rise to new sources of vulnerabilities (FSB, 2017[11]). Indeed, some algo-HFT strategies appear to have contributed to extreme market volatility, reduced liquidity and exacerbated flash crashes that have occurred with growing frequency over the past several years (OECD, 2019[12]) . In the absence of market makers willing to act as shock-absorbers by taking on the opposite side of transactions, such herding behaviour may lead to bouts of illiquidity, particularly in times of stress when liquidity is most important. Strategies based on deep neural networks can provide the best order placement and execution style that can minimise market impact (JPMorgan, 2019[8]). Deep neural networks mimic the human brain through a set of algorithms designed to recognise patterns, and are less dependent on human intervention to function and learn (IBM, 2020[9]).

We’ll discuss its applications in forecasting market trends, automating customer service and decision-making processes, and leveraging data science for better insights. Cybercrime costs the world economy around $600 billion annually (that is 0.8% of the global GDP). In this context, AI makes fraud detection faster, more reliable, and more efficient in financial services. Furthermore, they can identify patterns and detect anomalies that may indicate fraudulent activities.

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