What the Finance Industry Tells Us About the Future of AI

Fintech firms and other businesses around the world invested heavily in transforming to meet the needs of the new normal — remote working, social distancing and a business world changed perhaps forever. The value of AI is that it augments human capabilities and frees your employees up for more strategic tasks. Oracle’s AI is directly interactive with user behavior, for example, showing a list of the most likely values that an end-user would pick. Robust compute resources are necessary to run AI on a data stream at scale; a cloud environment will provide the required flexibility. 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 perspective falls short of reality, in that AI can be a critical enabler of finance’s “priorities” — such as more dynamic financial planning or close and consolidation efficiency.

Some banks are pushing ahead in the design of omnichannel journeys, but most will need to catch up. In the financial services business, 94 per cent of IT professionals polled stated they are unsure that their employees, advisers, and partners can properly handle consumer data. Fortunately, artificial intelligence can assist in reducing false positives and human mistakes. According to Forbes, “70% of all financial services organisations are already utilising machine learning to forecast cash flow occurrences, fine-tune credit ratings, and detect fraud.” In advanced deep learning models, issues may arise concerning the ultimate control of the model, as AI could unintentionally behave in a way that is contrary to consumer interests (e.g. biased results in credit underwriting).

Your finance department is at the core of the AI transformation

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. Ever since Facebook changed its name this month to Meta, the metaverse is all the world can talk about, and it’s not without good reason. While by and large, leaders are unsure precisely how the metaverse, a shared virtual space, will look in 2022 and beyond, there are some things that fintech firms should watch out for.

Gone are the days of relying entirely on traditional financial metrics as AI transforms how forecasters rate and dissect the financial health of companies. At the heart of this transformation is machine learning (ML), a powerful tool that enables  crunching vast data sets to detect and establish meaningful patterns. ML algorithms analyze historical financial data, market trends and economic indicators at astonishing speed and accuracy, identifying insights that elude traditional analytics and suggest a more nuanced understanding of an industry or a business’s  performance. Predictive analytics (PA), a subset of machine learning, leverages historical data to forecast future financial trends, enabling informed assumptions assessing revenue projections, market share or risks. Ensure financial services providers have robust and transparent governance, accountability, risk management and control systems relating to use of digital capabilities (particularly AI, algorithms and machine learning technology). The platform operating model envisions cross-functional business-and-technology teams organized as a series of platforms within the bank.

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The AI-first bank of the future will need a new operating model for the organization, so it can achieve the requisite agility and speed and unleash value across the other layers. While most banks are transitioning their technology platforms and assets to become more modular and flexible, working teams within the bank continue to operate in functional silos under suboptimal collaboration models and often lack alignment of goals and priorities. The AI-first bank of the future will also enjoy the speed and agility that today characterize digital-native companies. It will collaborate extensively with partners to deliver new value propositions integrated seamlessly across journeys, technology platforms, and data sets. Risk management is always a significant – and continuous – concern in banking (and practically every other industry). Machine learning can now assist specialists in identifying patterns, identifying hazards, conserving personnel, and ensuring better knowledge for future planning.

Consumer finance accounts for more than half of Chase’s net earnings; as a result, the bank has established essential fraud detection applications for its account users. Among the most important business cases for artificial intelligence in banking is its capacity to identify and prevent frauds and breaches. Consumers crave financial freedom, and the capacity to control one’s financial health is pushing the use of AI in personal finance.

What is AI in Finance?

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.

AI is increasingly adopted by financial firms trying to benefit from the abundance of available big data datasets and the growing affordability of computing capacity, both of which are basic ingredients of machine learning (ML) models. Financial service providers use these models to identify signals and capture underlying relationships in data in a way that is beyond the ability of humans. 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. 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.

These organizations recognize that AI performs some narrowly defined tasks better than people, but it cannot do everything better. In many cases, tasks that people perceive as simple are nearly impossible for a machine to replicate. Only 10% to 30% of organizations report that they’ve realized significant financial benefit from artificial intelligence. Insufficient ai in finance skills and employee acceptance are two of the top 3 leading causes for low returns on AI. Leading finance organizations exhibit a common pattern of actions and decisions that result in significant returns on AI initiatives. It helps businesses raise capital and handle automated marketing and messaging and uses blockchain to check investor referral and suitability.

Bank unlocks and analyzes all relevant data on customers via deep learning to help identify bad actors. It’s been using this technology for anti-money laundering and, according to an Insider Intelligence report, has doubled the output compared with the prior systems’ traditional capabilities. Alpaca uses proprietary deep learning technology and high-speed data storage to support its yield farming platform. (Yield farming is when cryptocurrency investors pool their funds to carry out smart contracts that gain interest.) Alpaca is compatible with dozens of cryptocurrencies and allows users to lend assets to other investors in exchange for lending fees and protocol rewards.

As we will explain, when these interdependent layers work in unison, they enable a bank to provide customers with distinctive omnichannel experiences, support at-scale personalization, and drive the rapid innovation cycles critical to remaining competitive in today’s world. Each layer has a unique role to play—under-investment in a single layer creates a weak link that can cripple the entire enterprise. On the one hand, banks need to achieve the speed, agility, and flexibility innate to a fintech.

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. The human parameter is critical both at the data input stage and at the query input stage and a degree of scepticism in the evaluation of the model results can be critical in minimising the risks of biased model decision-making. Human judgement is also important so as to avoid interpreting meaningless correlations observed from patterns as causal relationships, resulting in false or biased decision-making. It encourages financial education policy makers to cooperate with the authorities in charge of personal data protection frameworks and it identifies additional elements pertaining to personal data to complement the core competencies identified in the G20 OECD INFE Policy Guidance note.

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Using our own solutions, Oracle closes its books faster than anyone in the S&P 500—just 10 days or roughly half of the time taken by our competitors. This leaves our financial team with more time focused on the future instead of just reporting the past. Consumers are hungry for financial independence, and providing the ability to manage one’s financial health is the driving force behind adoption of AI in personal finance. Whether offering 24/7 financial guidance via chatbots powered by natural language processing or personalizing insights for wealth management solutions, AI is a necessity for any financial institution looking to be a top player in the industry.

• Documentation and audit trails are also held around deployment decisions, design, and production processes.
• The OECD has done this via its leading global policy work on financial education and financial consumer protection.
• Documentation of the logic behind the algorithm, to the extent feasible, is being used by some regulators as a way to ensure that the outcomes produced by the model are explainable, traceable and repeatable (FSRA, 2019[46]).