AI in banking
The banking industry was an early adopter of AI and continues to invest heavily in advanced AI applications using generative AI and related technologies. As a result, the impact of AI in banking has been substantial, enabling financial institutions to become more efficient, customer-centric and profitable. In this article, we explain why banks can no longer afford to remain on the sidelines of AI adoption. We also delve into three essential governance and ethical considerations of using AI in banking and share examples of how AI helps financial institutions remain competitive in a rapidly changing industry.
Why banks can’t ignore AI
Banks failing to prioritize the use of AI risk falling behind their peers. As competitors capitalize on the advantages of AI and customer demand for seamless, highly personalized services increases, the embrace of AI is no longer optional.
Customer expectations
With their focus on providing convenient, personalized digital experiences, online retailers have raised customer expectations across various industries, including banking. AI can help financial institutions efficiently provide superior, convenient and personalized service without having to invest in additional staff.
Industry leaders are using AI as a competitive advantage
Forward-thinking financial institutions have fully embraced the promise of AI, deeply integrating this technology into both front and back-end operations. Rather than limiting the use of AI to modest pilot projects, these innovators are using AI to build a significant competitive advantage in the form of risk management tools, robo-advisors and more.
New competitors in the market
New entrants into the financial services market are using AI and other technologies to capture market share, challenging the traditional dominance of brick-and-mortar banks. Neobanks are one example. These digital banks offer many of the same banking services as traditional banks with a customer-centered online banking experience. Others are embedding financial products and services within their platforms, allowing customers to access financial services such as sending and receiving money and accessing credit. As new sources of competition emerge, AI can help banks remain viable in the fast-evolving financial services industry.
AI governance and ethics for financial institutions
Financial institutions create, store and use vast amounts of sensitive data, which is then used by AI algorithms. Developing robust AI governance and ethical guidelines enables financial institutions to use this data responsibly, ensuring compliance with relevant regulatory standards while addressing the ethical implications of AI-driven decision-making.
What to consider
Although AI opens up numerous avenues for profitability and growth, it also introduces new risk elements. It’s essential that banks adapt their existing governance frameworks to account for these risks. Policies governing the use of AI must address themes such as explainability, accountability, data integrity, reliability, security and safety.
Establish policies proactively
Proactively addressing the potential legal, regulatory and ethical issues regarding the use of AI in banking ensures that data can be used to its full potential without running afoul of accepted uses. Planning ahead by establishing policies and training staff on security and governance also ensures AI initiatives align with the organization’s ethical standards, values and mission.
Implement appropriate data infrastructure
AI models require a robust, scalable data infrastructure that provides the compute power and storage capabilities needed to run resource-intensive AI applications. Financial institutions benefit from a fully managed cloud solution that places no additional burden on staff. With a fully managed solution, there is no hardware or software to select, install, configure or manage. Data security and governance tools should be built in, ensuring sensitive data maintained by a financial institution is protected from inappropriate access and tampering, helping banks to achieve and maintain regulatory compliance.
Generative AI in financial services
One of the most promising applications of AI in banking is the use of generative AI. Generative AI is a type of artificial intelligence that is capable of creating new content and understanding and responding to queries using natural language. This subset of AI has two immediate applications in the banking industry.
The first is its ability to enable business users to query their data in a conversational way. With generative AI, data users don't need to be proficient in SQL queries or develop Python code to access their enterprise data in meaningful ways. Business users can hold question-and-answer style conversations with their data system using natural language. This natural interaction model removes the technical barriers to data access, opening up new opportunities for user engagement and innovation.
Second, generative AI enhances the effectiveness of many traditional use cases of AI in banking. Unlike other AI methods that rely on smaller data sets and higher levels of human intervention, generative AI’s large language models (LLMs) leverage self-supervised learning to train on enormous quantities of diverse data, making them substantially more capable than many legacy AI models. Forward-looking financial institutions realize the potential that generative AI provides, using it to enhance the effectiveness of existing AI implementations and expand to new cases such as the ones discussed below.
Examples of AI in banking
From powering highly sophisticated risk management programs to creating more intelligent and responsive investment banking solutions, AI provides financial institutions with vital capabilities. The following real-world examples showcase just a few of the ways that artificial intelligence is reshaping the future of banking.
Customer service
Today’s conversational AI chatbots can provide instant customer support, answering queries, assisting with transactions and handling routine tasks. These increasingly dynamic AI-powered digital agents provide 24/7 support when and where needed, improving customer satisfaction, reducing operational costs and freeing human agents to focus on more complex support requests. Artificial intelligence can further enhance customer satisfaction by improving operational efficiencies such as routing customer calls to the correct departments and calculating appropriate customer hold times.
Robo-advisors and financial planning
Robo-advisors help investors make informed decisions. Digital advisors are driven by powerful AI algorithms that analyze vast data sets and market trends, incorporating a range of data sources that include an individual investor’s risk tolerance, financial goals and market conditions to create customized investment portfolios. Once an investment portfolio has been created, these robo-advisors continuously monitor portfolios, automatically rebalancing investment allocations based on real-time market data and adjustments to the investor’s short- or long-term investment goals.
Predictive analytics
AI-enabled predictive analytics tools empower banks to make highly accurate predictions about future events, such as cash flow and stock prices. By combining historical financial data and market trends with new data sources such as real-time market data, AI algorithms can help banks improve the precision of their predictions.
Risk management
AI can identify patterns, connections, and trends that are easily overlooked by human analysts. Risk management use cases include assessing a borrower’s credit risk, anti-money laundering programs, fraud prevention systems and the identification of potential investment risk scenarios.
Accelerate your AI transformation with Snowflake
AI is fundamentally altering how banks do business. Snowflake provides the cloud architecture and tools required to maximize the benefits of AI in banking. With the Financial Services Data Cloud, financial institutions can scale AI-driven banking workflows, from customer 360 and fraud detection to risk management and regulatory reporting, with a single copy of data. Snowflake’s built-in security features ensure organizations can protect sensitive data with strict controls to meet compliance and risk requirements.