Guide to AI in Finance

This guide covers the main applications of artificial intelligence for the financial services industry.

By The Scale Team on July 7th, 2023

Contents

    Introduction

    AI in finance is rapidly transforming how banks and other financial institutions perform investment research, engage with customers, and manage fraud. While traditional banking institutions are interested in incorporating new technologies, fintechs are adopting this technology more quickly as they try to catch up with larger institutions. To stay ahead of the game, larger financial institutions are investing heavily, with 77% planning to increase their budgets over the next three years, according to Scale's 2023 AI Readiness report.

    Financial Services institutions are looking to AI to help them improve customer experience, grow revenue, and improve operational efficiency. Many banks have found that implementing AI requires financial investment and machine learning expertise and tools to fine-tune models on proprietary data to maximize their investments and achieve their goals. In this guide, we will identify several opportunities to apply AI in finance and how to get started so you can stay ahead of the competition.

    AI for Finance: Why is it important?

    Financial Institutions have much to gain from implementing AI to improve revenues and reduce costs. Accenture estimates that Financial Services companies will add over $1 Trillion in value to global banks by 2035. McKinsey also estimates that AI can deliver up to $1 trillion in value to global banks annually. This significant impact is due to the complexity of financial transactions, enormous amounts of proprietary and third-party data, increasing fraudulent activity, and the large number of customers financial institutions service. 

    AI provides many benefits for the finance industry:

    Improved customer experience: 89% of financial services companies will use AI to improve the customer experience. AI has the potential to revolutionize finance by allowing companies to offer an array of personalized financial services at an affordable price. These companies will also be able to make it easier to learn more about the financial industry and their product offerings and reduce the friction to buying new products. Financial institutions can leverage their vast troves of data to offer personalized investment strategies, swiftly detect fraudulent activity, and efficiently assess fraud claims.

    Enhanced operational efficiency: AI accelerates the automation of many activities, such as identity verification, credit scoring, loan approval, and portfolio optimization. Drastically reducing manual effort while improving accuracy, AI enables financial institutions to pass the savings to customers through better prices, making them more competitive. 56% of those surveyed in our report identified operational efficiency as a goal for adopting AI at their organization.

    Increased profitability and revenue: 72% of financial services companies surveyed in our report identified growing revenue as a goal for adopting AI in their organization. With increased efficiency, financial institutions will cut costs and increase profits. Banks will increase revenue and have more stability by leveraging AI to make better investment decisions, optimize their portfolios, and mitigate risks. Wealth managers are increasing their efficiency by using AI copilots to summarize large amounts of financial data, automatically generate charts and visualizations, and create personalized portfolios leading to increased revenue at reduced costs.  

    Improved Fraud Detection: Consumers reported losing over $8 billion in fraud in 2022, with the actual total costs across banking being much higher. Fraud impacts banks' bottom lines and causes consumer prices to increase to offset the direct and indirect costs. AI promises to dramatically improve fraud detection and prevention capabilities by detecting trends and analyzing vast amounts of data, outperforming traditional fraud prevention solutions.

    We will now explore some of the top use cases of AI in Finance.

    AI in Finance: Use cases

    There are numerous applications for AI in Finance, with more likely to emerge in the next few years. For this guide, we will focus on the key data-centric areas identified in our 2023 Zeitgeist AI Readiness Report:

    1. Investment research

    2. Fraud detection and anti-money laundering

    3. ​​Customer-facing process automation

    4. Personalized assistants/chatbots

    5. Personalized portfolio analysis

    6. Exposure modeling

    7. Portfolio valuation

    8. Risk modeling

    Investment research

    AI has been a game-changer for financial analysts and wealth managers, completely altering the scale at which information can be gathered and analyzed. Automatically identifying, extracting, and analyzing relevant information from structured and unstructured data sources increases the quantity and relevancy of data that analysts and managers can incorporate into their processes, making them far more efficient and effective.

    Deploying cutting-edge AI tools like Scale's Enterprise Copilot helps analysts and wealth managers summarize large amounts of data, making them more effective and accurate advisors. Leveraging fine-tuned large language models with access to proprietary content, advisors can quickly summarize research and other data sources, create charts and visualizations of client portfolios, and ask for insights on massive knowledge bases with source citations, enabling them to investigate that source content further when necessary. Source content includes financial statements, historical data, news, social media, and research reports. With a Copilot, each Wealth Manager becomes many times more efficient and accurate in their work, multiplying their value to a financial services firm.

    Our 2023 Zeitgeist AI Readiness Report, reported that financial service companies use AI to summarize content, detect trends, and classify topics to improve investment decisions. We found that, among financial companies leveraging AI for investment research, 75% use it for content summarization, and 62% of companies use it for trend detection, which involves using AI to identify patterns in data:

    Financial services companies use data from financial statements, historical market data, 3rd party databases, social media content, news, and geospatial/satellite imagery to improve their models. Using AI to analyze these disparate data sources increasingly yields improved results that help these companies gain an edge.

    While many investment firms rely on fully or partially automated investment strategies, the best results are still achieved by keeping humans in the loop and combining AI insights with human analysts' reasoning capabilities.

    Fraud detection and anti-money laundering

    AI is proving its value to the finance industry in detecting and preventing fraudulent and other suspicious activity. In 2022, the total cost savings from AI-enabled financial fraud detection and prevention platforms was $2.7 billion globally, and the total savings for 2027 are projected to exceed $10.4 billion.

    AI-enabled fraud detection is particularly critical due to the rising fraud rates. The cost of eCommerce fraud alone is projected to surpass $48 billion worldwide in 2023, compared to just over $41 billion in the previous year. Furthermore, fraudsters are becoming more sophisticated and difficult to identify using conventional, rule-based approaches, making it challenging for financial institutions to meet anti-money laundering compliance requirements.

    Financial institutions can use ML algorithms to identify fraudulent transactions to spot anomalies in large datasets. A single transaction has a vast number of associated data points, such as location, time, merchant identity, and past spending behavior, and the complexity of this data poses a formidable challenge for manual or rule-based analysis.

    Customer-facing process automation

    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. Optical character recognition (OCR) allows for instant digitization of checks, receipts, and invoices, while AI-powered facial recognition can effortlessly determine whether there is a match between a customer's ID and a selfie while simultaneously confirming that the ID is legitimate. 

    Aside from chatbots and virtual assistants, ML-powered NLP is a powerful tool for extracting relevant information from documents and generating reports and personalized financial advice. Automating routine tasks reduces the number of tedious tasks to be done by humans (and the associated operating costs) and minimizes human error. The ability to generate automatic reports from data is valuable to both customers and regulators, enhancing both personalization and compliance in a scalable way. With RPA increasingly handling the more mundane tasks, skilled employees can focus on more valuable tasks, leading to greater job satisfaction.

    Personalized assistants and chatbots

    With the proliferation of financial services firms and offerings, providing good customer service is crucial to maintaining customer engagement and satisfaction. However, the expectation of immediate and round-the-clock assistance makes relying solely on live agents impractical and costly. Fortunately, recent breakthroughs in conversational AI, such as those demonstrated by ChatGPT, have resulted in chatbots that more closely approximate human responses. Powered by generative large language models, these chatbots excel at understanding intent and can redirect customers to human representatives when needed.

    While large language models like OpenAI's GPT-4 and Anthropic's Claude work well out of the box, many financial institutions find that they need to customize models to get them to provide the best responses and align with their policies. Techniques like fine-tuning models on proprietary data, prompt engineering, and retrieval help elevate a base model from acceptable responses to a superior customer experience. Many financial institutions leverage their vast data to offer AI-enabled personalized service and guidance. Institutions can provide customers with assistant-like features, including categorizing expenditures, suggesting savings goals and strategies, and providing notice about upcoming transfers. AI can offer personalized financial advice and guidance based on individual customer profiles and preferences and assist users with budgeting, financial planning, and investment decisions.

    Financial institutions also leverage AI-powered copilots like Scale's Enterprise Copilot to assist wealth managers internally. These copilots enable wealth managers to extract insights from internal and external documents, enabling informed decisions quickly and efficiently based on large volumes of data. By incorporating copilots into their workflow, wealth managers can significantly enhance their productivity and deliver more valuable insights. These copilots use fine-tuned base models with even greater access to proprietary data than customer-facing chatbots since copilots are meant for authorized employees. This means the copilots are even more powerful, providing a productivity boost for wealth managers while increasing customer satisfaction as investors get personalized advice more quickly.

    Personalized portfolio analysis

    Robo-advisors are gaining popularity as inflation rates soar, providing a simple and accessible option for passive investing. These automated wealth management platforms use AI to tailor portfolios to each customer's disposable income, risk tolerance, and financial goals. All the investor needs to do is complete an initial survey to provide this information and deposit the money each month - the robo-advisor picks and purchases the assets and re-balances the portfolio as needed to help the customer meet their targets.

    With increasingly more capable machine learning models, robo-advisors can analyze more data and provide more personalized investment plans. These models can analyze individual portfolios and provide insights into asset allocation, risk diversification, and performance evaluation. They can even suggest adjustments to optimize portfolio performance based on the customer's goals, risk tolerance, and market conditions. Also, robo-advisors can adapt to changing market dynamics and provide real-time portfolio analysis.

    Many robo-advisory platforms also support socially responsible investing (SRI), which has proven attractive for younger investors. These systems can allocate investments according to individual preferences, including or excluding certain asset classes in line with the customer's stated values. For instance, a robo-advisor can automatically curate a personalized portfolio for an investor who wishes to support companies that meet environmental, social, and governance (ESG) criteria or exclude those that sell harmful or addictive substances.

    Robo-advisors appeal to those interested in investing but lack the technical knowledge to make investment decisions independently. Much cheaper than human asset managers, they are a popular choice for first-time investors with a small capital base.

    Exposure modeling

    Exposure modeling estimates the potential losses or impacts a financial institution, or portfolio may experience under different market conditions. It aims to quantify a portfolio's potential vulnerabilities and sensitivities to various risk factors. Exposure modeling involves analyzing the relationship between the portfolio's holdings and different market variables to assess how changes in those variables can affect the portfolio's value or performance.

    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. Because AI can model and assess the potential financial exposure to risks such as market fluctuations, credit defaults, and economic events, as well as analyze historical data, market trends, and external factors to estimate potential losses or gains, it's a valuable tool for helping financial institutions make informed decisions regarding risk management and hedging strategies. Optimizing strategies using instruments like equity derivatives and interest-rate swaps may allow institutions to optimize portfolios and offer better prices to customers.

    Machine learning can be incorporated into exposure modeling in numerous ways. By analyzing vast amounts of historical financial data to identify patterns and correlations that may be difficult for humans to detect, models can learn and identify potential risks associated with specific market conditions or events. These models can also simulate various risk scenarios and generate probabilistic outcomes, allowing financial institutions to evaluate the potential impact of different market shocks on their portfolios. It may help uncover hidden risks that traditional models may overlook.

    By leveraging financial models, institutions can make faster and more informed decisions in response to changing market conditions. To extract relevant insights, They can use models to analyze unstructured data sources, such as news articles, social media feeds, and research reports. By understanding and processing textual information, these models can identify emerging risks, sentiment trends, or market-moving events that could impact exposure levels.

    Portfolio Valuation

    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.

    Many financial institutions are incorporating AI into their portfolio valuation processes to address these challenges. Financial institutions can enhance accuracy, efficiency, and decision-making with ai-powered asset valuation that is automated and accurate. These models can instantly consider factors such as historical market data, current market behavior, pricing models, proprietary research, and performance indicators.

    By leveraging large volumes of financial data, including historical market data, company financials, economic indicators, and news sentiment, models can help companies identify patterns, correlations, and trends that impact portfolio valuation. Financial institutions can also integrate alternative data sources such as satellite imagery, social media, and consumer behavior data into portfolio valuation models to enrich the analysis.

    Risk modeling

    Accurate risk modeling is critical for financial institutions. These institutions must employ risk modeling to assess and quantify overall risk by analyzing exposure, probability, and potential impact. Risk modeling aims to capture and measure the various types of risks the institution faces and to provide a comprehensive view of the potential downside or volatility associated with those risks.

    Because of the complexities involved in risk modeling, this is an area where AI can have a substantial impact. AI enables financial institutions to develop more capable risk models based on large quantities of data, identifying complex patterns that are difficult for humans to replicate. Machine learning models can yield more accurate predictions, allowing financial services firms to manage risk more effectively.

    An important subset of risk modeling is credit scoring. Credit scoring powered by machine learning has proven invaluable for the finance industry, enabling rapid and accurate assessments with reduced bias. The key is using AI to assess potential borrowers based on alternative data such as rent payment history, job function, and financial behavior. Not only does this result in more accurate risk analysis by considering important indicators, but it also enables potential borrowers without a credit history to be assessed. 

    AI-based credit scoring has other clear advantages, such as reducing manual workload and increasing customer satisfaction with rapid credit card and loan application processing. 

     

    How to implement AI in finance

    When companies implement AI for any use case, it's essential to establish a carefully considered strategy. Finance companies should tie their AI goals to business problems and develop a solid data strategy. In Scale's annual Zeitgeist: AI Readiness Report, we surveyed over 1,600 ML practitioners and business leaders and found that an organization's goals shape the effectiveness of its AI implementation. Finance companies must ensure that the goals of an AI implementation, such as growing revenue, improving operational efficiency, or enhancing customer experience, are aligned with company priorities. 

    We suggest adhering to the following steps throughout the implementation process:

    1. Prioritize your use cases: What are the top challenges that you are facing, and what are your company's top priorities? Are you focused on increasing revenue, improving customer experience, or improving operational efficiency? Do you need to improve investment research, fraud detection, or portfolio valuation? Dig deep into defining the problems that you are trying to solve.

    2. Define a robust data strategy: Once you have prioritized your use cases, the most important thing you can do is to define a robust data strategy. Any AI solution is only as good as the data available. While off-the-shelf base models are impressive at general tasks, they don't perform well on specific finance tasks and don't have access to proprietary data. To improve performance on these tasks, open-source or commercial foundation models must be fine-tuned on your proprietary data. Your internal knowledge-base data, including research reports, historical market data, and customer data, must be accessible to the models for fine-tuning and retrieval. Determine what data you have, the formats in which you need that data, and what it will take to clean and standardize your existing data and improve your data collection mechanisms. For knowledge retrieval, you will need to chunk your text data, convert it into embeddings, store it in vector databases, and perform a similarity search to retrieve that data for the model to incorporate in responses. Doing this correctly and at scale is challenging, and this is a constantly evolving space, so you will need to stay up to date with the latest research and open-source and commercial capabilities.

    3. Baseline internal capabilities: As machine learning technology advances rapidly, it is essential to understand your internal capabilities. Do you have the internal machine learning expertise to implement an AI strategy properly? Do you have a data strategy and the capabilities and tools to implement that strategy in the near term? Do you have the partners to help you implement your strategy effectively? Before you make significant investments, it is critical to understand this clearly.

    4. Consider security: Companies in the financial industry regularly work with a variety of confidential and proprietary data. Popular cloud-based models can leak confidential data and pose other security and safety risks, so it's crucial to ensure you're protecting your data. Only use tools and applications that align with your company's security policies.

    5. Build a "crawl, walk, run" methodology: When building an AI solution for finance, start small by addressing a specific challenge or customer need. Then, innovate quickly and test various solutions using proof of concept implementations or product pilots. Expand your solution to incorporate new use cases based on their impact on company priorities.

    Read the guide Generative AI for the Enterprise: From Experimentation to Production for more detailed steps on implementing Generative AI.

    Conclusion

    This guide covered the most prominent use cases and applications for AI in finance. We covered investment research, fraud detection and anti-money laundering, customer-facing process automation, personalized assistants/chatbots, personalized portfolio analysis, exposure modeling, portfolio valuation, and risk modeling.

    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. 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.

    If you're interested in learning more about how to apply AI for your financial services business, Scale EGP (Enterprise Generative AI Platform) provides a full-stack generative AI platform for your enterprise. For additional details on how to implement Generative AI, read the guide Generative AI for the Enterprise: From Experimentation to Production.