The Impact of Generative AI in Finance Deloitte US

Crypto, NFTs and digital tokens are taking on a whole new life, and the way finance is done online is changing. Facebook’s name change could prove more than just a rebranding but instead suggests a much bigger development is at hand. 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. Today’s digital assistants are context-aware, conversational, and available on almost any device.

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. It should be noted that the massive take-up of third-party or outsourced AI models or datasets by traders could benefit consumers by reducing available arbitrage opportunities, driving down margins and reducing bid-ask spreads. At the same time, the use of the same or similar standardised models by a large number of traders could lead to convergence in strategies and could contribute to amplification of stress in the markets, as discussed above. Such convergence could also increase the risk of cyber-attacks, as it becomes easier for cyber-criminals to influence agents acting in the same way rather than autonomous agents with distinct behaviour (ACPR, 2018[13]). To choose the technologies that will reinforce your business in the future, the best thing to do is start strategically planning how this technology will fit in your overall business plan.

Detection, management, and prevention of fraud

On the other, they must continue managing the scale, security standards, and regulatory requirements of a traditional financial-services enterprise. Over several decades, banks have continually adapted the latest technology innovations to redefine how customers interact with them. The 2000s saw broad adoption of 24/7 online banking, followed by the spread of mobile-based “banking on the go” in the 2010s. According to Built In, AI technologies are assisting banks and lenders in making “smarter underwriting judgments” throughout the loan and credit card acceptance process. This is accomplished through the use of a number of characteristics that provide a more realistic image of individuals who may be traditionally underserved.

• They prioritize using artificial intelligence to help individuals do their jobs better rather than using AI to improve the productivity of departments or functions.
• Either they are still in the planning phase for AI implementation, or they don’t have a plan at all.
• The decision for financial institutions (FIs) to adopt AI will be accelerated by technological advancement, increased user acceptance, and shifting regulatory frameworks.
• 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.
• 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.

Given the investment required by firms for the deployment of AI strategies, there is potential risk of concentration in a small number of large financial services firms, as bigger and more powerful players may outpace some of their smaller rivals (Financial Times, 2020[6]). Such investment is not constrained in monetary resources required to be invested in AI technologies but also relates to talent and staff skills involved in such techniques. Such risk of concentration is somewhat curbed by the use of third-party vendors; however, such practice raises other challenges related to governance, accountability and dependencies on third parties (including concentration risk when outsourcing is involved) (see Section 2.3.5). With AI poised to handle most manual accounting tasks, the development and proficiency of higher-level skills will be imperative to success for the next generation of finance leaders. Finance professionals will still need to be proficient in the fundamentals of finance and accounting to oversee the algorithms and be able to spot anomalies. However, their day-to-day work will increasingly focus less on crunching the numbers and more on data interpretation, business analysis, and communication with key stakeholders.

AI Companies in Financial Credit Decisions

Policy makers and regulators have a role in ensuring that the use of AI in finance is consistent with promoting financial stability, protecting financial consumers, and promoting market integrity and competition. Emerging risks from the deployment of AI techniques need to be identified and mitigated to support and promote the use of responsible AI without stifling innovation. 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. The difficulty in decomposing the output of a ML model into the underlying drivers of its decision, referred to as explainability, is the most pressing challenge in AI-based models used in finance. In addition to the inherent complexity of AI-based models, market participants may intentionally conceal the mechanics of their AI models to protect their intellectual property, further obscuring the techniques.

Gartner estimates that by 2027, nearly all of the growth in worldwide IT spending will come from software and IT services. Aside from the “Magnificent Seven” stocks, investors are seeking other opportunities that may provide similarly lucrative returns. However, with so many tech sector executives touting the term “AI” on their earnings calls or during media interviews, it can be challenging to discern which companies may actually be emerging as leaders in the space.

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]). Major applications of DLTs in financial services include issuance and post-trade/clearing and settlement of securities; payments; central bank digital currencies and fiat-backed stablecoins; and the tokenisation of assets more broadly. Merging AI models, criticised for their opaque and ‘black box’ nature, with blockchain technologies, known for their transparency, sounds counter-intuitive in the first instance. As such, rather than provide speed of execution to front-run trades, AI at this stage is being used to extract signal from noise in data and convert this information into trade decisions. As AI techniques develop, however, it is expected that these algos will allow for the amplification of ‘traditional’ algorithm capabilities particularly at the execution phase. AI could serve the entire chain of action around a trade, from picking up signal, to devising strategies, and automatically executing them without any human intervention, with implications for financial markets.

Similarly, in times of over-performance, users are unable to understand why the successful trading decision was made, and therefore cannot identify whether such performance is due to the model’s superiority and ability to capture underlying relationships in the data or to other unrelated factors. That said, there is no formal requirement for explainability for human-initiated trading strategies, although the rational underpinning these can be easily expressed by the trader involved. ai in finance 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.

While these systems automate financial processes, they require significant manual maintenance, are slow to update, and lack the agility of today’s AI-based automation. Unlike rule-based automation, AI can handle more complex scenarios, including the complete automation of mundane, manual processes. Traditionally, financial processes, such as data entry, data collection, data verification, consolidation, and reporting, have depended heavily on manual effort.

Third, banks will need to redesign overall customer experiences and specific journeys for omnichannel interaction. This involves allowing customers to move across multiple modes (e.g., web, mobile app, branch, call center, smart devices) seamlessly within a single journey and retaining and continuously updating the latest context of interaction. Leading consumer internet companies with offline-to-online business models have reshaped customer expectations on this dimension.

Its underwriting platform uses non-tradeline data, adaptive AI models and records that are refreshed every three months to create predictive intelligence for credit decisions. First, banks will need to move beyond highly standardized products to create integrated propositions that target “jobs to be done.”8Clayton M. Christensen, Taddy Hall, Karen Dillon and David S. Duncan, “Know your customers ‘jobs to be done,” Harvard Business Review, September 2016, hbr.org. Further, banks should strive to integrate relevant non-banking products and services that, together with the core banking product, comprehensively address the customer end need. An illustration of the “jobs-to-be-done” approach can be seen in the way fintech Tally helps customers grapple with the challenge of managing multiple credit cards. Few would disagree that we’re now in the AI-powered digital age, facilitated by falling costs for data storage and processing, increasing access and connectivity for all, and rapid advances in AI technologies.

For Customers

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 addition, the use of ‘off-the-shelf’ algorithms by a large part of the market could prompt herding behaviour, convergence and one-way markets, further amplifying volatility risks, pro-cyclicality, and unexpected changes in the market both in terms of scale and in terms of direction. 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.

AI integration in blockchains could in theory support decentralised applications in the DeFi space through use-cases that could increase automation and efficiencies in the provision of certain financial services. 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]). In other words, AI can be used to extract and process information of real-time systems and feed such information into smart contracts. As in other blockchain-based financial applications, the deployment of AI in DeFi augments the capabilities of the DLT use-case by providing additional functionalities; however, it is not expected to radically affect any of the business models involved in DeFi applications. Artificial intelligence in finance refers to the application of a set of technologies, particularly machine learning algorithms, in the finance industry. This fintech enables financial services organizations to improve the efficiency, accuracy and speed of such tasks as data analytics, forecasting, investment management, risk management, fraud detection, customer service and more.

ServiceNow is quietly building the next generation of its IT software solutions, powered by generative AI.

AI models executed on a blockchain can be used to execute payments or stock trades, resolve disputes or organize large datasets. The platform validates customer identity with facial recognition, screens customers to ensure they are compliant with financial regulations and continuously assesses risk. Additionally, the platform analyzes the identity of existing customers through biometric authentication and monitoring transactions.

• This makes them incompatible with existing regulation that may require algorithms to be fully understood and explainable throughout their lifecycle (IOSCO, 2020[39]).
• It will collaborate extensively with partners to deliver new value propositions integrated seamlessly across journeys, technology platforms, and data sets.
• 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.

While infratech can include a number of technologies, AI and ML applications are of note, particularly as digital technologies become more integrated into structures, changing the nature of infrastructure from simple hard assets to dynamic information systems (G20 Saudi Arabia, 2020[30]). For example, AI can be a powerful tool to optimise windmill operations and safety, analyse traffic patterns in transportation, and improve operations in energy grids. 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.

They can then translate these insights into a transformation roadmap that spans business, technology, and analytics teams. Reimagining the engagement layer of the AI bank will require a clear strategy on how to engage customers through channels owned by non-bank partners. All of this aims to provide a granular understanding of journeys and enable continuous improvement.10Jennifer Kilian, Hugo Sarrazin, and Hyo Yeon, “Building a design-driven culture,” September 2015, McKinsey.com. Despite billions of dollars spent on change-the-bank technology initiatives each year, few banks have succeeded in diffusing and scaling AI technologies throughout the organization.

While the company may not attract the same amount of attention as other leading software providers, the trends in its customer base combined with the overall evolution of its total addressable market create a compelling investment opportunity. Increasingly, customers expect their bank to be present in their end-use journeys, know their context and needs no matter where they interact with the bank, and to enable a frictionless experience. Numerous banking activities (e.g., payments, certain types of lending) are becoming invisible, as journeys often begin and end on interfaces beyond the bank’s proprietary platforms. For the bank to be ubiquitous in customers’ lives, solving latent and emerging needs while delivering intuitive omnichannel experiences, banks will need to reimagine how they engage with customers and undertake several key shifts.

For example, embracing new technologies that enable drastic reductions in greenhouse gas (GHG) emissions when building and operating infrastructure will be a crucial element to net zero emissions. This could be from the type of cement that is used to installation of energy efficient charging stations for electric vehicles. Governments, in cooperation with diverse stakeholders, could benefit from sharing good practices related to technology and innovation in infrastructure, while also setting supportive policy frameworks to harness the benefits while mitigating risks. Smart contracts are at the core of the decentralised finance (DeFi) market, which is based on a user-to-smart contract or smart-contract to smart-contract transaction model.