Published by Jessica Weisman-Pitts
Posted on October 15, 2024
9 min readLast updated: January 29, 2026
Written by Adam Darby
October 2024
To remain relevant in a competitive marketplace, it’s imperative for today’s banking and financial services institutions to constantly evolve. Generative artificial intelligence (AI) and other emerging technologies have revolutionized how companies leverage critical data, engage with customers, protect personal information, and execute daily operations. AI-automated data analysis helps organizations better understand customers’ behaviors and unique needs. Due to this growing emphasis on innovative digital solutions, AI investments and implementations increase yearly. In 2023, the financial services industry invested an estimated $35 billion in AI, with banking leading the charge, accounting for approximately $21 billion. At its current growth rate, the financial sector’s spending on AI is predicted to reach $97 billion by 2027.
To fully embrace the future of AI in the banking and financial world, it is essential to understand the reasons behind these large-scale investments and the keys to successful implementation. Vinuraj Devaraj is a senior technical analyst for a global financial software organization with 20 years of fintech expertise in data analytics, machine learning (ML), business intelligence, and software development and maintenance. With an extensive background in quantitative research, data analytics, data engineering, data modeling, and data architecture, Devaraj has a unique perspective on how integrating AI into operations can transform financial services organizations from the inside out.
Q: How would you describe the impact and benefits of AI and ML on the financial services industry?
Devaraj: The progression of AI and ML started with big data. Big data concerns were popular from 2008 to 2010 because of the processing power of various systems. These original models laid the groundwork for the approach taken in developing today’s ML tools and solutions. The last decade was revolutionary with efforts to achieve cost efficiency, operational efficiency, and delivery. Forward-looking organizations have adopted AI solutions with these goals driving their digital transformation. This was seen across multiple industries, but the predominant modeling and remarkable adoption of ML is especially apparent in the financial services and healthcare industries. Many of these service industries have utilized automation tools that support ML models to enhance their cost and operational efficiency.
In financial services, a significant benefit of AI and ML is the reduction of “human-intensive” transactions. Previously, to open an account, a person walked into a branch, talked to a bank employee, filled out account paperwork, and waited for the background checks to be completed before the banker relayed the decision on whether the person was eligible to open an account. That involved a lot of time on both sides. Today, ML models help financial institutions automate their operations, allowing preliminary account creation processes and requirements to be completed quicker so customers can use their new accounts sooner. Another example of a reduction human-intensive interactions can be seen in mortgage underwriting. The traditional underwriting process is time consuming, paperwork heavy, and labor-intensive. Today, organizations can develop ML models and then integrate them with robotics and rules to ensure that mortgage underwriting is completed quickly, efficiently, and accurately.
The use of AI in enabling customer behavior pattern analysis for financial markets and investment businesses has advanced significantly. Organizations are increasingly adopting AI/ML models to gain valuable insights into customer actions, interests, needs, and values. This provides companies with opportunities to improve marketing strategies, develop personalized recommendations, and enhance overall customer satisfaction by catering to customers’ unique needs. Through a deep analysis of customer patterns and behavior, financial institutions can create stronger connections and enhance client engagement. This improves the quality of service and increases customer loyalty. Organizations build more esteemed reputations and grow their businesses by leveraging AI to improve the customer experience.
Q: What are the challenges of adopting and implementing AI initiatives within established financial services organizations?
Devaraj: One challenge occurs when financial institutions fail to educate their customers about the effectiveness of communicating with automated agents while emphasizing that trust remains the same as that of human interaction. Meanwhile, there are inherent issues with customer transparency regarding AI. For example, it is important to inform customers about the potential mistakes or errors that may occur with AI initiatives and their many functions.
When financial institutions alter various customer-facing business processes with AI/ML solutions, it is crucial to inform longtime customers accustomed to a particular process about the many benefits and the occasional hindrances. This creates a stable relationship built on honesty and transparency. For example, the automated AI agent communicating with the customer must relay essential information, inquiries, or commands to a professional to provide the necessary human input. If customers understand how this process works, they feel assured that their information is in safe hands and their needs are being met through automated AI/ML engagement. This ensures that organizations uphold quality standards while remaining reliable. It’s imperative for organizations to learn how to best utilize automated customer engagement while allowing AI technologies to learn from each customer interaction to better maintain quality and customer loyalty.
Another challenge is what occurs when there is an overreliance on outdated legacy systems containing sensitive information and foundational data. Customers want to know their financial data is safe and secure. Data privacy and cybersecurity remain top priorities in modern business, especially regarding finances. While these legacy systems often struggle with handling large volumes of data efficiently, integrated ML algorithms can automate data processing tasks, reduce errors, and increase speed to create a safer, more secure environment for the dataset. Automated privacy standards and tasks are established to guarantee enhanced data security. AI applications can also provide a deep analysis to extract valuable insights from data that is otherwise overlooked by the original system.
Q: What are the most common use cases for AI/ML solutions in banking and finance?
Devaraj: Several use cases are transforming the industry. For example, banks can leverage AI/ML models that help create accurate credit scores based on credit data to confirm fast and efficient automated scoring. This simplifies the process and guarantees a data-driven credit score. It also further identifies eligibility for purchases such as homes or automobiles dependent on a specific credit range. Rather than worry about the possibility of human error, customers can rely on data analysis to provide a more precise score.
Another use case is fraud detection and prevention. AI-enabled data protection strategies ensure that personal information doesn’t fall into the wrong hands. Sometimes, it’s challenging for humans to execute trustworthy data protection successfully. Standardized protection and security processes reassure customers that their financial data remains safe even when transferred between professionals and multiple agencies. Automated security can safeguard sensitive information by adhering to established compliance standards created by human input and sending compliance reports to financial professionals and agencies to ensure they agree on data safety.
Other use cases include virtual agents or assistants used by investment banks. For example, Bank of America’s Erica virtual assistant communicates with customers about areas of high or abnormal expenditures, check account security, temporarily lock/unlock cards, schedule automatic payments, locate specific account information, and other common functions. Integrating AI can significantly improve how customers manage finances while potentially reducing expenses associated with customer service staffing.
Q: Why is it essential to have extensive compliance standards and cybersecurity strategies when integrating AI technologies within an organization?
Devaraj: Financial cybersecurity compliance is serious business. The nature of handling highly sensitive data including financial records and personal information makes financial institutions prime targets for cybercriminals. A breach can lead to operational disruptions, reputational damage, lawsuits, criminal responsibility, and non-compliance fines. A robust cybersecurity and compliance strategy protects customer data and ensures the integrity of financial transactions. AI/ML models support this strategy by detecting threats in real time, improving fraud protection, assessing risks, and responding to incidents automatically. This mitigates cyberthreats while adhering to regulatory standards and requirements. Models can also be programmed to monitor systems based on industry-specific requirements, including anti-money laundering and know your customer standards. Most importantly, because these models are continuously learning, they can adapt to evolving threats and changing regulations.
Q: What are your predictions for AI usage within financial services?
Devaraj: We are seeing a shift to conversational and portable AI. Customers are engaging with virtual agents and bots when seeking assistance with account management, financial advice, and market updates, to name a few. Robotics is another crucial aspect of AI development. In addition to robotic process automation that can enable certain tasks, customers may be able to interact with in-branch robots in the future. Drones are another area where robotics could facilitate property inspections, for example. Innovations will also provide faster, easier, and more informative ways to enhance customer education about how AI models and customer data are used, how they develop their predictive models, and the implications of using customer data in this development. This creates a greater sense of trust and transparency, providing peace of mind for the clientele.
Leveraging AI/ML solutions for customer retention and business growth
AI and ML innovations have a significant impact on how financial services organizations handle big data and operations. Global organizations are increasingly investing in the many benefits that AI solutions offer in today’s fast-paced market. While human engagement and interactions will always be essential for successful financial service organizations, automated processes and skillfully implemented data models will seamlessly integrate into modern business infrastructures to provide faster processing, more accurate data, and fully satisfied customers.
About the Author:
Adam Darby is a freelance writer for various magazines and news publications. He also serves as a content writer for professional websites, blogs, podcasts, and social media accounts. For additional information, contact adamdarby4@gmail.com.
Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think and learn. AI can perform tasks such as data analysis, customer service, and decision-making.
Machine learning is a subset of AI that enables systems to learn from data and improve their performance over time without being explicitly programmed. It is widely used in data analysis and predictive modeling.
Big data refers to large and complex data sets that traditional data processing software cannot handle efficiently. It is used in various industries to analyze patterns, trends, and insights.
Fraud detection is the process of identifying and preventing fraudulent activities within financial transactions. It often involves the use of AI and machine learning to analyze patterns and anomalies.
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