As the “tip of the spear” in generative AI, finance can build the strategy that fully considers all the opportunities, risks, and tradeoffs from adopting generative AI for finance. Quantitative trading is the process of using large data sets to identify patterns that can be used to make strategic trades. AI-powered computers can analyze large, complex data sets faster and more efficiently than humans. An industrial goods company has a prospective customer that requests a line of credit to purchase its products.
- Documentation and audit trails are also held around deployment decisions, design, and production processes.
- The key is using AI to assess potential borrowers based on alternative data such as rent payment history, job function, and financial behavior.
- 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.
- If the tool had identified any red flags, the credit analyst would have needed to validate the information before incorporating it into the final credit decision.
Currently, financial market participants rely on existing governance and oversight arrangements for the use of AI techniques, as AI-based algorithms are not considered to be fundamentally different from conventional ones (IOSCO, 2020). Model governance best practices have been adopted by financial firms since the emergence of traditional statistical models for credit and other consumer finance decisions. Documentation and audit trails are also held around deployment decisions, design, and production processes. As AI continues to shape the financial services landscape, it’s crucial that finance companies rapidly invest in AI innovation. Fintechs and traditional banking institutions are investing in this technology, and it promises to give them an edge in revenue growth, improved customer experiences, and operational efficiency.
Learn how to transform your essential finance processes with trusted data, AI-insights and automation. Guardrails to ensure ethics, regulatory compliance, transparency and explainability—so that stakeholders understand the decisions made by the financial institution—are essential in order to balance the benefits of AI with responsible and accountable use. By establishing oversight and clear rules regarding its application, AI can continue to evolve as a trusted, powerful tool in the financial industry. As with any artificial intelligence solution, the best use cases exploit a specific business’s strengths and defend its weaknesses. Aligning generative AI’s fundamental capabilities to your business’s unique strategies and objectives delivers a value that differentiates your company from its competitors.
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Powered by generative large language models, these chatbots excel at understanding intent and can redirect customers to human representatives when needed. Automation using AI is essential for the financial services industry to meet customer demands for better personalization and enhanced features while reducing costs. By automating repetitive, manual tasks such as document digitization, data entry, and identity verification, financial institutions can expand their offerings to maintain a competitive edge.
Considering the interconnectedness of asset classes and geographic regions in today’s financial markets, the use of AI improves significantly the predictive capacity of algorithms used for trading strategies. 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).
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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. For Chase, consumer banking represents over 50% of its net income; as such, the bank has adopted key fraud detecting applications for its account holders. For example, it has implemented a proprietary algorithm to detect fraud patterns—each time a credit card transaction is processed, details of expense report software the transaction are sent to central computers in Chase’s data centers, which then decide whether or not the transaction is fraudulent. Chase’s high scores in both Security and Reliability—largely bolstered by its use of AI—earned it second place in Insider Intelligence’s 2020 US Banking Digital Trust survey. The decision for financial institutions (FIs) to adopt AI will be accelerated by technological advancement, increased user acceptance, and shifting regulatory frameworks.
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But, the adoption of generative AI in finance functions entails challenges, including accuracy and data security and privacy. To overcome the obstacles and stay ahead of the adoption curve, now is the time for CFOs to learn about the applications of generative AI in finance functions that will have the most impact and prepare to capitalize on emerging capabilities. Valuing a portfolio is crucial for assessing its performance, making investment decisions, and reporting accurate financial information to stakeholders. However, manual valuation can be challenging as various factors influence portfolio value, including market data, pricing models, time horizon, and allocation of diverse investment types such as stocks, bonds, mutual funds, derivatives, and other securities. 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.
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When developing AI solutions, you should follow best practices by following frameworks that emphasize identifying desired outcomes, ensuring you have implemented a solid data strategy, and then experimenting and implementing scalable AI solutions. Companies should tie their goals for AI in finance to business problems and identify performance metrics based on these goals. New models are developing rapidly, and companies in the finance industry need to adapt to new technology quickly. Financial institutions are increasingly using AI for exposure modeling in finance to assess and manage various types of risks that financial institutions face. Exposure modeling involves estimating the potential losses a firm may experience under different market conditions, such as changes in interest rates, credit defaults, or market volatility. Optimizing strategies using instruments like equity derivatives and interest-rate swaps may allow institutions to optimize portfolios and offer better prices to customers.
Blue Dot is an AI tax compliance platform that uses patented technology to help businesses ensure tax compliance. Reduce tax vulnerabilities for consumer-style spending and get a 360-degree view of all employee-driven transactions. If you’re not using AI tools for accounting tasks, you’re making things more complicated than they need to be. According to a Gartner study, 80% of CFOs surveyed in 2022 expected to spend more on AI in the coming two years.2 With that investment, however, around two-thirds think their function will reach an autonomous state within six years. 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.
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). Challenges also exist with regards to the legal status of smart contracts, as these are still not considered to be legal contracts in most jurisdictions (OECD, 2020). Until it is clarified whether contract law applies to smart contracts, enforceability and financial protection issues will persist. At the single trader level, the lack of explainability of ML models used to devise trading strategies makes it difficult to understand what drives the decision and adjust the strategy as needed in times of poor performance. Given that AI-based models do not follow linear processes (input A caused trading strategy B to be executed) which can be traced and interpreted, users cannot decompose the decision/model output into its underlying drivers to adjust or correct it.
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). 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). The scaling up of the use of algorithms that generate uncorrelated profits or returns may generate correlation in unrelated variables if their use reaches a sufficiently important scale. It can also amplify network effects, such as unexpected changes in the scale and direction of market moves.
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The widespread adoption of AI and ML by the financial industry may give rise to some employment challenges and needs to upgrade skills, both for market participants and for policy makers alike. Demand for employees with applicable skills in AI methods, advanced mathematics, software engineering and data science is rising, while the application of such technologies may result in potentially significant job losses across the industry (Noonan, 1998) (US Treasury, 2018). Such loss of jobs replaced by machines may result in an over-reliance in fully automated AI systems, which could, in turn, lead to increased risk of disruption of service with potential systemic impact in the markets. With increasingly more capable machine learning models, robo-advisors can analyze more data and provide more personalized investment plans.
Auditing mechanisms of the model and the algorithm that sense check the results of the model against baseline datasets can help ensure that there is no unfair treatment or discrimination by the technology. Ideally, users and supervisors should be able to test scoring systems to ensure their fairness and accuracy (Citron and Pasquale, 2014). Tests can also be run based on whether protected classes can be inferred from other attributes in the data, and a number of techniques can be applied to identify and/or rectify discrimination in ML models (Feldman et al., 2015).