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.
- This is why finance will be one of the first areas to see the impact of these technologies on day-to-day activities—in everything from automating payments to calculating risk—with detailed analytics that automatically audit processes and alert teams to exceptions.
- These models analyze historical data, identify patterns, and predict the likelihood of default or delinquency.
- The company’s applications also helped increase automation, accelerate private clouds and secure critical data at scale while lowering TCO and futureproofing its application infrastructure.
It focuses on data-related issues, the lack of explainability of AI-based systems; robustness and resilience of AI models and governance considerations. The complexity of delivering unbiased and valid financials demands that people remain engaged in the automation loop. AI-forward finance functions design AI-driven processes so that automated steps and decisions are observable and that people can interrupt an automated process and supplement actions with human judgment. Darktrace’s AI, machine learning platform analyzes network data and creates probability-based calculations, detecting suspicious activity before it can cause damage for some of the world’s largest financial firms. Alpaca uses proprietary deep learning technology and high-speed data storage to support its yield farming platform.
Reshoring manufacturing to the US: The role of AI, automation and digital labor
Ocrolus’ software analyzes bank statements, pay stubs, tax documents, mortgage forms, invoices and more to determine loan eligibility, with areas of focus including mortgage lending, business lending, consumer lending, credit scoring and KYC. There is no universally accepted definition of AI, and the term is construed differently across various industries, sectors and jurisdictions. The European Commission has proposed a ‘technology neutral’ definition of ‘artificial intelligence system’ which is intended to be flexible and future proof to account for developments in the technology. That definition is “software that is developed with one or more [specified] techniques and approaches [e.g. Machine learning] and can, for a given set of human-defined objectives, generate outputs such as content, predictions, recommendations, or decisions influencing the environments they interact with”. The integration of AI and ML in finance is enabling algorithmic trading systems to continuously learn and adapt to market conditions.
This paradigm shift enables financial institutions to deliver superior services, enhancing customer experiences and outcomes. In the realm of personalized financial services, AI in finance is reshaping how institutions operate. By 2030, the adoption of AI in the financial services sector is expected to add $1.2 trillion in value, according to a report by McKinsey & Company.
3.4. Training, validation and testing of AI models to promote their robustness and resilience
Automating middle-office tasks with AI has the potential to save North American banks $70 billion by 2025. Further, the aggregate potential cost savings for banks from AI applications is estimated at $447 billion by 2023, with the front and middle office accounting for $416 billion of that total. This is just the beginning of AI in finance and will continue to grow in importance over time.
What is ML in finance?
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. To attract this key talent, AI-forward CFOs adjust their the difference between cash transfers and in-kind benefits recruitment strategies, develop new career paths and invest in data science technologies and development opportunities for current staff. These CFOs also adjust their hiring focus to create talent pipelines and develop trainings for candidates with nontraditional finance backgrounds. Here are a few examples of companies providing AI-based cybersecurity solutions for major financial institutions.
Retail Credit Scoring
AI techniques could further strengthen the ability of BigTech to provide novel and customised services, reinforcing their competitive advantage over traditional financial services firms and potentially allowing BigTech to dominate in certain parts of the market. 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. Similar considerations apply to trading desks of central banks, which aim to provide temporary market liquidity in times of market stress or to provide insurance against temporary deviations from an explicit target. As outliers could move the market into states with significant systematic risk or even systemic risk, a certain level of human intervention in AI-based automated systems could be necessary in order to manage such risks and introduce adequate safeguards. Potential consequences of the use of AI in trading are also observed in the competition field (see Chapter 4). Traders may intentionally add to the general lack of transparency and explainability in proprietary ML models so as to retain their competitive edge.
Once their doubts and concerns have been addressed, these virtual agents can guide customers through the purchase process eliminating the need to navigate a menu and taking them straight to the desired product. For example, because AI uncovers more issues for auditors to ask their clients about, auditors gain more insight into their clients’ fiscal responsibility and complete their work more efficiently. Also, because there is an expected increase in demand for making sense of corporate data or sustainability reporting, the number of accounting business data analysts and business consultants in accounting firms is likely to increase. A financial institution procuring the development of an AI system or tool with a view to placing it on the market or putting it into service under its own name or trade mark, will be considered a ‘provider’ of AI and would be required to comply with the applicable regulations. This would also be the case where a financial institution or provider develops its own AI system.
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. Traditional data anonymisation approaches do not provide rigorous privacy guarantees, as ML models have the power to make inferences in big datasets. The use of big data by AI-powered models could expand the universe of data that is considered sensitive, as such models can become highly proficient in identifying users individually (US Treasury, 2018[32]). Facial recognition technology or data around the customer profile can be used by the model to identify users or infer other characteristics, such as gender, when joined up with other information. In some jurisdictions, comparative evidence of disparate treatment, such as lower average credit limits for members of protected groups than for members of other groups, is considered discrimination regardless of whether there was intent to discriminate. 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]).
