💸 FundrCap's AI Lens: AI in Finance
Money, Machines, and the Future: Exploring the Role of AI in Finance
The Future of Finance: How AI is shaping the industry
The finance industry is undergoing a rapid transformation as the power of artificial intelligence (AI) is increasingly harnessed to drive business growth and innovation. The trend is expected to continue, with global spending on AI predicted to double between 2020 and 2024, reaching over USD 110 billion. AI is being used in various areas of finance, including retail and corporate banking, asset management, trading, and insurance, as well as in regulatory and supervisory applications.
Industry Overview
The global AI in finance market is expected to grow at a compound annual growth rate (CAGR) of 35.2% from 2021 to 2028, reaching $26.43 billion by the end of the forecast period. The increasing adoption of AI in finance is driven by the growing demand for automation, the need for fraud detection and prevention, and the need for better customer experiences. 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.
The deployment of AI in finance can improve efficiency, reduce costs, increase productivity, and enhance the quality and personalization of financial products and services. However, it can also amplify existing risks or give rise to new challenges, such as biases, herding behavior, and operational risks. The lack of explainability of AI-based systems and the difficulty in understanding how they generate results can also create compliance and market integrity risks.
Policy makers need to support responsible AI innovation in the financial sector, while ensuring that investors and financial consumers are protected and the markets remain fair, orderly, and transparent. They should consider the potential transformative effects of AI on specific financial market activities, as well as the benefits, challenges, and risks associated with its use. Policy makers should address data-related issues, enhance the explainability, robustness, and resilience of AI models, and establish clear governance frameworks to ensure accountability.
Policy recommendations that can assist policy makers in supporting AI innovation in finance include strengthening the quality and availability of data, promoting explainability and transparency, enhancing model governance frameworks, establishing clear accountability frameworks, promoting international cooperation and coordination, and supporting research and development efforts. By addressing the emerging risks associated with the deployment of AI in finance, policy makers can promote innovation while safeguarding the stability and integrity of financial markets.
AI Applications
Financial firms are increasingly turning to AI as a means of taking advantage of the large amounts of available big data and the growing affordability of computing capacity. Both of these factors are essential components of machine learning (ML) models. By using these models, financial service providers can detect signals and uncover relationships in data that go beyond human capabilities. The applications of AI in finance go far beyond ML models for decision-making and encompass a broad range of activities in the financial market. According to research published by Autonomous NEXT in 2018, the implementation of AI has the potential to reduce operating costs in the financial services industry by 22% by 2030.
Asset management and the buy-side
The use of AI techniques in asset management and buy-side market activities has the potential to create efficiencies and reduce costs in operational workflows, strengthen risk management, and improve decision-making around portfolio allocation. The use of big data and ML models trained on such datasets provides a new informational edge to investors who use AI to digest vast datasets and unlock insights that inform their strategies at short timeframes. However, there is a risk of concentration in a small number of large financial services firms due to the investment required for the deployment of AI strategies, which could lead to increased homogeneity in the market, herding behavior, and increased volatility in times of stress. The use of third-party vendors raises challenges related to governance, accountability, and dependencies on third parties, including concentration risk when outsourcing is involved.
Algorithmic Trading
The use of AI in trading has become increasingly common, providing traders with tools for identifying and defining trading strategies, making decisions based on predictions, executing transactions without human intervention, managing liquidity, enhancing risk management, better organizing order flows, and streamlining execution. AI-powered algorithms add complexity to traditional algorithmic trading, evolving into fully automated, computer-programmed algorithms that learn from the data input used and rely less on human intervention. Additionally, the use of ML models shifts the analysis towards prediction and real-time trend analysis instead of conventional back-testing strategies based on historical data. AI techniques are expected to allow for the amplification of traditional algorithm capabilities throughout the entire chain of action around a trade, from picking up signal, to devising strategies, and automatically executing them without any human intervention.
Credit intermediation and assessment of creditworthiness
AI is transforming credit intermediation and assessment of creditworthiness by providing advanced analytics models that increase speed, reduce costs, and improve risk management. AI-powered credit scoring models that use big data can analyze creditworthiness for clients with limited credit history or insufficient collateral, enabling greater financial inclusion and supporting the growth of the real economy. However, the use of ML models and big data for credit underwriting raises risks of disparate impact in credit outcomes and potential discrimination or unfair lending. Biased or discriminatory outcomes of AI credit rating models can be unintentional, resulting from inadequate or inappropriate data, lack of transparency or explainability of AI-based models, or inherent biases in the data used as variables. Regulations and safeguarding mechanisms such as auditing, testing, disclosure, and governance frameworks can mitigate such risks.
AI in blockchain based financial services
AI and blockchain are two transformative technologies that can revolutionize financial services. The combination of AI and blockchain can improve security, increase efficiency, and enhance the customer experience in the financial services industry. The benefits of integrating AI and blockchain include improved security, increased efficiency, and enhanced customer experience. However, there are also challenges to integrating these technologies, including regulatory compliance, adoption, and interoperability. The future of AI and blockchain in financial services looks bright as technology advances and regulations change, and we can expect to see new and innovative financial services leveraging the power of AI and blockchain.
Risks and Challenges
From customer service to risk management and fraud detection, AI is becoming increasingly critical for financial institutions. However, the deployment of AI in finance also presents new risks and challenges that must be addressed.
Risks Associated with AI in Finance
One of the most significant risks associated with AI in finance is the potential for bias. AI models learn from historical data, and if that data is biased, the models will be biased as well. This can result in unfair lending practices, for example, that disproportionately affect certain groups of people. Another risk associated with AI in finance is the possibility of cybersecurity breaches. AI systems are only as secure as the networks they operate on, and sophisticated hackers could potentially access sensitive financial information through AI systems.
Regulatory Challenges
Another challenge associated with the deployment of AI in finance is regulatory compliance. As AI becomes increasingly prevalent in financial services, regulators are struggling to keep up with the rapid pace of technological change. This has led to a lack of regulatory guidance, which can make it difficult for financial institutions to implement AI solutions in a compliant manner. Moreover, regulators will need to consider the ethical and societal implications of AI deployment, including potential job displacement and unequal access to financial services.
Ethical Considerations
Ethical considerations are also a significant challenge associated with AI in finance. For instance, the use of AI in lending decisions can have a disproportionate impact on marginalized groups. Additionally, financial institutions must ensure that their AI systems are transparent, explainable, and fair, and they must be able to show that they have conducted appropriate due diligence on their AI systems. Lack of transparency can erode customer trust, and financial institutions could face reputational damage as a result.
Operational Risks
AI in finance also presents operational risks. AI systems are only as good as the data they are trained on, and if the data is incomplete or inaccurate, the models will not be effective. Moreover, AI systems require significant computing power and storage, and they must be maintained and updated regularly. This can create operational challenges for financial institutions, especially those with limited IT resources.
Data Privacy and Security
Data privacy and security are also major concerns when it comes to AI in finance. Financial institutions are responsible for safeguarding their customers' financial information, and they must ensure that their AI systems comply with applicable data privacy regulations. Furthermore, AI systems must be designed to be secure, and financial institutions must take steps to protect their systems from cyber threats.
Exemplary Players
DataRobot - DataRobot provides an AI platform that automates predictive modeling for financial services companies. DataRobot - DataRobot has raised over $1 billion in funding, with investors including Altimeter Capital, T. Rowe Price, and Silver Lake.
AlphaSense - AlphaSense provides an AI-powered search engine that helps financial analysts quickly find relevant information. AlphaSense has raised a total of $150 million in funding, with investors including Innovation Endeavors and Tribeca Venture Partners.
Kasisto - Kasisto provides an AI-powered chatbot for financial institutions to improve customer engagement and reduce operational costs. Kasisto has raised a total of $88.5 million in funding, with investors including Oak HC/FT, Propel Venture Partners, and Two Sigma Ventures.
Kensho - Kensho provides an AI platform that helps financial institutions make better investment decisions by analyzing large amounts of data. Kensho has raised a total of $67.5 million in funding, with investors including Goldman Sachs, JPMorgan Chase, and Baidu.
ZestAI - ZestAI provides an AI platform for credit underwriting, helping lenders make more accurate risk assessments. ZestFinance has raised a total of $215 million in funding, with investors including Lightspeed Venture Partners, Peter Thiel, and WestCap Group.
Nauto - Nauto provides an AI platform for the insurance industry, helping insurers better understand driver behavior and improve risk management. Nauto has raised a total of $173.9 million in funding, with investors including Greylock Partners, Softbank Vision Fund, and General Motors Ventures.
SymphonyAI Sensa - SymphonyAI Sensa provides an AI platform for anti-money laundering (AML) compliance, helping financial institutions reduce fraud and comply with regulatory requirements. Symphony AyasdiAI has raised a total of $105 million in funding, with investors including GE Ventures, Khosla Ventures, and Institutional Venture Partners (IVP).
Lendbuzz - Lendbuzz uses AI to assess credit risk and provide loans to international students and expats in the United States. The company has raised a total of $412 million, with investors including 83North, Viola Credit, and Migdal Insurance.
Better.com - Better.com uses AI to streamline the mortgage application and approval process. The company has raised a total of $1.5 billion, with investors including SoftBank Vision Fund, Kleiner Perkins, and Goldman Sachs.
Conclusion
The finance industry is on the cusp of a major transformation, driven by the increasing adoption of AI. While there are risks and challenges associated with the adoption of AI, the benefits of AI in finance cannot be ignored. As businesses look to leverage the power of AI to drive growth and innovation, it is important to stay up-to-date with the latest trends and best practices in the industry. With the right approach and investment, AI has the potential to revolutionize the finance industry and create new opportunities for businesses and customers alike.
Sources
OECD Business and Finance Outlook 2021 : AI in Business and Finance - OECD
Artificial Intelligence in Financial Services: Applications and benefits of AI in finance - Business Insider
The impact of artificial intelligence in the banking sector & how AI is being used in 2022 - Business Insider
Artificial Intelligence in Finance: Opportunities and Challenges - Towards Data Science
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