Artificial intelligence in financial services Deloitte Insights

However, the use-cases of AI in finance are not restricted to ML models for decision-making and expand throughout the spectrum of financial market activities (Figure 2.1). Research published in 2018 by Autonomous NEXT estimates that implementing AI has the potential to cut operating costs in the financial services industry by 22% by 2030. The deployment of AI techniques in finance can generate efficiencies by reducing friction costs (e.g. commissions and fees related to transaction execution) and improving productivity levels, which in turn leads to higher profitability. In particular, the use of automation and technology-enabled cost reduction allows for capacity reallocation, spending effectiveness and improved transparency in decision-making. AI applications for financial service provision can also enhance the quality of services and products offered to financial consumers, increase the tailoring and personalisation of such products and diversify the product offering. The use of AI mechanisms can unlock insights from data to inform investment strategies, while it can also potentially enhance financial inclusion by allowing for the analysis of creditworthiness of clients with limited credit history (e.g. thin file SMEs).

DataRobot provides machine learning software for data scientists, business analysts, software engineers, executives and IT professionals. DataRobot helps financial institutions and businesses quickly build accurate predictive models that inform decision making around issues like fraudulent credit card transactions, digital wealth management, direct marketing, blockchain, lending and more. Alternative lending firms use DataRobot’s software to make more accurate underwriting decisions by predicting which customers have a higher likelihood of default. The implementation of AI applications in blockchain systems is currently concentrated in use-cases related to risk management, detection of fraud and compliance processes, including through the introduction of automated restrictions to a network.

Different methods are being developed to reduce the existence of irrelevant features or ‘noise’ in datasets and improve ML model performance, such as the creation of artificial or ‘synthetic’ datasets generated and employed for the purposes of ML modelling. These can be extremely useful for model testing and validation purposes in case the existing datasets lack scale or diversity (see Section 1.3.4). The difficulty in comprehending, following or replicating the decision-making process, referred to as lack of explainability, raises important challenges in lending, while making it harder to detect inappropriate use of data or the use of unsuitable data by the model.

What leading AI finance organizations do differently

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. One report found that 27 percent of all payments made in 2020 were done with credit cards. Clients receive 24/7 access to proven management and technology research, expert advice, benchmarks, diagnostics and more. Our recent survey shows that four out of five finance leaders anticipate the cost and effort they allocate to deploying AI within finance will increase over the next two years, with 52% of these leaders anticipating cost and effort to increase by more than 10%.

This portfolio approach likely enabled frontrunners to accelerate the development of AI solutions through options such as AI-as-a-service and automated machine learning. At the same time, through crowdsourced development communities, they were able to tap into a wider pool of talent from around what is an assignment of contract the world. In short, it means that companies will likely invest heavily in unlocking and understanding the data they have and seek to acquire more to make smart business decisions. However, it’s not just the quantity of data that matters, it’s the quality of the analysis that counts.

• For many IT departments, ERP systems have often meant large, costly, and time-consuming deployments that might require significant hardware or infrastructure investments.
• Using AI to unlock the potential in the finance sector offers limitless possibilities.
• It has been argued that at this stage of development of the infrastructure, storing data off chain would be a better option for real time recommendation engines to prevent latency and reduce costs (Almasoud et al., 2020[27]).
• Companies can also look at making best-in-class and respected internal services available to external clients for commercial use.

Solid governance arrangements and clear accountability mechanisms are indispensable, particularly as AI models are increasingly deployed in high-value decision-making use-cases (e.g. credit allocation). Organisations and individuals developing, deploying or operating AI systems should be held accountable for their proper functioning (OECD, 2019[52]). Importantly, intended outcomes for consumers would need to be incorporated in any governance framework, together with an assessment of whether and how such outcomes are reached using AI technologies. 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.

Lead and shape the future of finance

Identifying the appropriate AI technology approach for a specific business process and then combining them could lead to better outcomes. Frontrunners have taken an early lead in realizing better business outcomes (figure 8), especially in achieving revenue enhancement goals, including creating new products and pursuing new markets. While many financial services companies agree that AI could be critical for building a successful competitive advantage, the difference in the number of respondents in the three clusters that acknowledged the critical strategic importance of AI is quite telling (figure 3). Deloitte refers to one or more of Deloitte Touche Tohmatsu Limited, a UK private company limited by guarantee (“DTTL”), its network of member firms, and their related entities. In the United States, Deloitte refers to one or more of the US member firms of DTTL, their related entities that operate using the “Deloitte” name in the United States and their respective affiliates. Certain services may not be available to attest clients under the rules and regulations of public accounting.

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The most disruptive potential of AI in trading comes from the use of AI techniques such as evolutionary computation, deep learning and probabilistic logic for the identification of trading strategies and their automated execution without human intervention. Contrary to systematic trading, reinforcement learning allows the model to adjust to changing market conditions, when traditional systematic strategies would take longer to adjust parameters due to the heavy human involvement. AI, ML, and natural language processing (NLP) help financial institutions identify borrowing patterns to reduce the risk of non-repayment.

Title:AI in Finance: Challenges, Techniques and Opportunities

Boston Consulting Group partners with leaders in business and society to tackle their most important challenges and capture their greatest opportunities. Today, we work closely with clients to embrace a transformational approach aimed at benefiting all stakeholders—empowering organizations to grow, build sustainable competitive advantage, and drive positive societal impact. Many of the most important current opportunities reside outside of the finance function. CFOs should work with their C-suite peers to encourage creative thinking around potential use cases that promote cost efficiency and effectiveness. CFOs can also collaborate with financial planning and analysis and business partners to allocate investments to generative AI and incorporate generative AI-influenced cost targets into the business plan. Generative Al’s large language models applied to the financial realm marks a significant leap forward.

Algorithmic trading

Robotic process automation (RPA) can eliminate time-intensive and error-prone work, such as entering customer data from contracts, forms, and other sources. Plus, AI technologies and RPA bots can handle banking workflows more accurately and efficiently than humans. An industrial goods company has a prospective customer that requests a line of credit to purchase its products. Because the company does not know the customer, it must conduct a comprehensive credit review before proceeding. The company’s traditional credit review process sought to identify problematic legal or business issues by gathering information from the customer supplemented with additional data collected through third-party sources and internet searches.

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AI can be used to reduce (but not eliminate) security susceptibilities and help protect against compromising of the network, for example in payment applications, by identifying irregular activities for instance.. Similarly, AI applications can improve on-boarding processes on a network (e.g. biometrics for AI identification), as well as AML/CFT checks in the provision of any kind of DLT-based financial services. AI applications can also provide wallet-address analysis results that can be used for regulatory compliance purposes or for an internal risk-based assessment of transaction parties (Ziqi Chen et al., 2020[26]). AI tools and big data are augmenting the capabilities of traders to perform sentiment analysis so as to identify themes, trends, patterns in data and trading signals based on which they devise trading strategies. While non-financial information has long been used by traders to understand and predict stock price impact, the use of AI techniques such as NLP brings such analysis to a different level.

Generative AI empowers faster and better data-driven decisions based on historical data, market trends and the use of AI foundation models that identify patterns and anomalies often missed by traditional analysis methods. In a recent AI News article, Mani Nagasundaram, senior vice president and head of solutions of global financial services at HCL Technologies, explained that COVID-19 has forced banks and financial institutions to respond to customers at an even faster pace — and around the clock. Artificial intelligence can free up personnel, improve security measures and ensure that the business is moving in the right technology-advanced, innovative direction. Possible risks of concentration of certain third-party providers may rise in terms of data collection and management (e.g. dataset providers) or in the area of technology (e.g. third party model providers) and infrastructure (e.g. cloud providers) provision. AI models and techniques are being commoditised through cloud adoption, and the risk of dependency on providers of outsourced solutions raises new challenges for competitive dynamics and potential oligopolistic market structures in such services.

The data advantage of BigTech could in theory allow them to build monopolistic positions, both in relation to client acquisition (for example through effective price discrimination) and through the introduction of high barriers to entry for smaller players. This section looks at how AI and big data can influence the business models and activities of financial firms in the areas of asset management and investing; trading; lending; and blockchain applications in finance. All respondents were required to be knowledgeable about their company’s use of AI technologies, with more than half (51 percent) working in the IT function. Sixty-five percent of respondents were C-level executives—including CEOs (15 percent), owners (18 percent), and CIOs and CTOs (25 percent). In the past, to develop any sort of tech solution, a business would need a team of IT specialists to take charge of every element from inception to implementation.