QuantumBlack, McKinsey’s AI arm, helps companies transform using the power of technology, technical expertise, and industry experts. With thousands of practitioners at QuantumBlack (data engineers, data scientists, product managers, designers, and software engineers) and McKinsey (industry and domain experts), we are working to solve the world’s most important AI challenges. QuantumBlack Labs is our center of technology development and client innovation, which has been driving cutting-edge advancements and developments in AI through locations across the globe. As we have explored, navigating the complexities of AI integration necessitates a comprehensive approach that fosters responsible development and implementation. In this regard, EY has demonstrated its commitment to responsible AI development with its platform, EY.ai, launched in September 2023 with an investment of US$1.4 billion.
Challenges, risks and opportunities of AI in banking: an overview
Leading corporate and investment banks, for example, have built up expert teams of quants, modelers, translators, and others who often have AI expertise and could add gen AI skills, such as prompt engineering and database curation, to their capability set. Banks with fewer AI experts on staff will need to enhance their capabilities through some mix of training and recruiting—not a small task. One of the most notable applications of AI in finance is its role in enhancing investment strategies. AI-driven robo-advisors are becoming increasingly popular, providing personalized investment advice based on sophisticated algorithms that analyze vast amounts of data. These robo-advisors consider factors such as market trends, economic indicators and individual client preferences to create tailored investment plans. Synthetic data could also lead to a better customer experience through the designing and testing of new propositions, such as loans or investments.
With machine learning technologies, computers can be taught to analyze data, identify hidden patterns, make classifications, and predict future outcomes. The learning comes from these systems’ ability to improve their accuracy over time, with or without direct human supervision. Machine learning typically requires technical experts who can prepare data sets, select the right algorithms, and interpret the output. Our surveys also show that about 20 percent of the financial institutions studied use the highly centralized operating-model archetype, centralizing gen AI strategic steering, standard setting, and execution. About 30 percent use the centrally led, business unit–executed approach, centralizing decision making but delegating execution.
Risk management for gen AI remains in the early stages for financial institutions—we have seen little consistency in how most are approaching the issue. Sooner rather than later, however, banks will need to redesign their risk- and model-governance frameworks and develop new sets of controls. Again, the unstructured nature of much of the data and the size of the data sets add complexity to pinpointing quality issues. Leading banks are using a combination of human talent and automation, intervening at multiple points in the data life cycle to ensure quality of all data.
- This view can cover everything from highly transformative business model changes to more tactical economic improvements based on niche productivity initiatives.
- Starters and followers should probably brace themselves and start preparing for encountering such risks and challenges as they scale their AI implementations.
- The integration of AI into the cybersecurity framework of the banking sector encapsulates the technology’s dual nature as both a potential risk factor and a critical defensive tool.
- User experience could help alleviate the “last mile” challenge of getting executives to take action based on the insights generated from AI.
- Major banks, especially those in North America, have been pioneers in this journey, making substantial investments in AI to spearhead innovation, talent development and operational transparency.
Successful gen AI scale-up—in seven dimensions
With existing vendor relationships and technology platforms already in use, this is likely the easiest option for most companies to choose. To boost the chances of adoption, companies should consider incorporating behavioral science techniques while developing AI tools. Companies could also identify opportunities to integrate AI into varied user life cycle activities. While working on such initiatives, it is important to also assign AI integration targets and collect user feedback proactively.
Future-proofing through scalability and integration
AI is also transforming financial planning and advisory services by providing advisors with advanced tools to better understand their clients’ needs and goals. AI-driven analytics can assess a client’s financial health, predict future financial scenarios and recommend strategies to achieve long-term financial objectives. Overall, the integration of AI in finance is creating a new era of data-driven decision-making, efficiency, security and customer experience in the financial sector. Understand what’s top of mind for financial services companies as they decide where to host their AI infrastructure. The rise of GenAI also brings forth challenges such as cultural resistance within organizations, strategic misalignment and the need to balance the costs of innovation against returns on investment. Ensuring the governance of AI through ethical frameworks, data privacy measures and protection mechanisms is paramount to sustaining trust and compliance.
Investments in executive education will equip them to show employees precisely how the technology and the bank’s operations connect, thereby generating excitement and overcoming trepidation. You should consult with a licensed professional for advice concerning your specific situation. Explore the main themes that emerged in the results, including data issues and recruitment of AI experts. Learn how the c-suite views the AI capabilities of their company compared to the developers building the applications. To how to calculate the provision for income taxes on an income statement further demystify the new technology, two or three high-profile, high-impact value-generating lighthouses within priority domains can build consensus regarding the value of gen AI. This structure—where a central team is in charge of gen AI solutions, from design to execution, with independence from the rest of the enterprise—can allow for the fastest skill and capability building for the gen AI team.