AI in Finance 2022: Applications & Benefits in Financial Services

On the training side, we have to make sure we are feeding the right kind of data into AI tools—that we aren’t feeding data with a lot of “one-off” numbers, which would then become normalized. Reinforcement learning involves the learning of the algorithm through interaction and feedback. In a recent Harris Poll of workers, about half do not trust the technology.3 Finance leaders should consider change management carefully, leaning into the idea that generative AI can support our lives, transforming from an enabler of our work to a potential co-pilot. Generative AI might start by producing concise and coherent summaries of text (e.g., meeting minutes), converting existing content to new modes (e.g., text to visual charts), or generating impact analyses from, say, new regulations. Producing novel content represents a definitive shift in the capabilities of AI, moving it from an enabler of our work to a potential co-pilot. Dmitry Dolgorukov is the Co-Founder and CRO of HES Fintech, a leader in providing financial institutions with intelligent lending platforms.

Eno was the very first language of choice SMS text-based companion delivered by a US bank when it launched in 2017. Let’s look at some of the important sectors of the financial business where artificial intelligence is having the most influence and adding value over traditional ways. Artificial intelligence (AI) is no longer a newcomer, and the discipline is evolving at a rapid rate. Almost every day, there is a new discovery, whether it is a research study introducing a new or enhanced machine learning algorithm or a new library with one of the most widely used programming languages.

Personalized banking/financial management

Moving ERP to the cloud allows businesses to simplify their technology requirements, have constant access to innovation, and see a faster return on their investment. Insider Intelligence estimates both online and mobile banking adoption among US consumers will rise by 2024, reaching 72.8% and 58.1%, respectively—making AI implementation critical for FIs looking to be successful and competitive in the evolving industry. The recent entry of large, well-established companies into the generative AI market has kicked off a highly competitive race to see who can deliver revolutionary value first. But in the rush to exploit this new capability, companies must consider the risks and impacts of using AI-driven technology to perform tasks that, until recently, were exclusively reserved for humans.

The use of AI to build fully autonomous chains would raise important challenges and risks to its users and the wider ecosystem. In such environments, AI contracts rather than humans execute decisions and operate the systems and there is no human intervention in the decision-making or operation of the system. In addition, the introduction of automated mechanisms that switch off the model instantaneously (such as kill switches) is very difficult in such networks, not least because of the decentralised nature of the network. To make sound decisions, it will be crucial that leaders consider the use of generative AI from an enterprise-wide approach with a clear understanding of where this technology will have an impact on operating expenditures, capital expenditures, market capitalization, and a lot more. CFOs and Finance leaders can play a pivotal role in driving strategic collaboration among key C-suite leaders to enable greater success—and return on investment—of AI deployment and adoption.

Companies Using AI in Personalized Banking

Looked at a different way, while ServiceNow is growing its enterprise customers at a healthy clip, its best days may be ahead. The Commission has already moved to begin setting up an AI Office that will oversee the compliance of a subset of more powerful foundational models deemed to pose systemic risk. It also recently announced a package of measures intended to boost the prospects of homegrown AI developers, including retooling the bloc’s network of supercomputers to support generative AI model training. The European ai in finance Union’s AI Act, a risk-based plan for regulating applications of artificial intelligence, has passed what looks to be the final big hurdle standing in the way of adoption after Member State representatives today voted to confirm the final text of the draft law. Gartner analysts examined 23 AI use cases in corporate finance representing the types of processes a future-looking autonomous finance organization will work on. They were ranked according to their business value and feasibility of implementation (see Figure 1).

• By analyzing intricate patterns in transaction data sets, AI solutions allow financial organizations to improve risk management, which includes security, fraud, anti-money laundering (AML), know your customer (KYC) and compliance initiatives.
• And since Finance draws upon enormous amounts of data, it’s a natural fit to take advantage of generative AI.
• Ocrolus offers document processing software that combines machine learning with human verification.
• AI and blockchain are both used across nearly all industries — but they work especially well together.
• The platform utilizes natural language processing to analyze keyword searches within filings, transcripts, research and news to discover changes and trends in financial markets.

Developed economies have regulations in place to ensure that specific types of data are not being used in the credit risk analysis (e.g. US regulation around race data or zip code data, protected category data in the United Kingdom). Regulation promoting anti-discrimination principles, such as the US fair lending laws, exists in many jurisdictions, and regulators are globally considering the risk of potential bias and discrimination risk that AI/ML and algorithms can pose (White & Case, 2017[22]). What is more, the deployment of AI by traders could amplify the interconnectedness of financial markets and institutions in unexpected ways, potentially increasing correlations and dependencies of previously unrelated variables (FSB, 2017[11]).

AI’s human-like outputs may seem like an obvious benefit to a productivity-minded manager, but employees perceive artificial intelligence as an employment threat. Our research revealed that 70% of the active workforce believes AI can replace people — so it’s not surprising when new AI-driven solutions are rejected and fail to gain traction. Many data science professionals still view finance as a necessary but uninteresting back-office function. Leading CFOs look to the AI generation — data science talent who are developing, deploying or championing the first wave of AI solutions — to fill the roles that contribute to successful finance AI deployments.

The identification of converging points, where human and AI are integrated, will be critical for the practical implementation of such a combined ‘man and machine’ approach (‘human in the loop’). The increasing use of complex AI-based techniques and ML models will warrant the adjustment, and possible upgrade, of existing governance and oversight arrangements to accommodate for the complexities of AI techniques. Explicit governance frameworks that designate clear lines of responsibility for the development and overseeing of AI-based systems throughout their lifecycle, from development to deployment, will further strengthen existing arrangements for operations related to AI. Internal governance frameworks could include minimum standards or best practice guidelines and approaches for the implementation of such guidelines (Bank of England and FCA, 2020[44]).

Principle 8: Protection of Consumer Data & Privacy

Additionally, Wealthblock’s AI automates content and keeps investors continuously engaged throughout the process. The smart app can cancel money-wasting subscriptions, find better options for services like insurance, and even negotiate bills. Additionally, 41 percent said they wanted more personalized banking experiences and information.

• 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]).
• Because AI is becoming increasingly prevalent across many industries, it’s no wonder that it’s taking off in the field of banking, especially now that COVID-19 has transformed human contact.
• 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.
• Distributed ledger technologies (DLT) are increasingly being used in finance, supported by their purported benefits of speed, efficiency and transparency, driven by automation and disintermediation (OECD, 2020[25]).
• Eno was the very first language of choice SMS text-based companion delivered by a US bank when it launched in 2017.

These technologies can lead to higher automation and, when deployed after controlling for risks, can often improve upon human decision making in terms of both speed and accuracy. The potential for value creation is one of the largest across industries, as AI can potentially unlock $1 trillion of incremental value for banks, annually (Exhibit 1). In the most advanced AI techniques, even if the underlying mathematical principles of such models can be explained, they still lack ‘explicit declarative knowledge’ (Holzinger, 2018[38]). This makes them incompatible with existing regulation that may require algorithms to be fully understood and explainable throughout their lifecycle (IOSCO, 2020[39]). The Task Force is currently conducting a strategic Review of the Principles to identify new or emerging developments in financial consumer protection policies or approaches over the last 10 years that may warrant updates to the Principles to ensure they are fully up to date.