Sailesh Sewpaul is the founder and executive chairman of PEX Ltd.
The use of artificial intelligence in the banking and financial services sector isn’t novel; it’s been occurring for decades. Banks have been utilizing computer models to evaluate creditworthiness and credit risk, detect fraudulent activity on accounts and automate processes for many years. But those rudimentary models based on pre-defined rulesets are rapidly becoming obsolete compared to the current capabilities of AI.
As artificial intelligence continues to evolve and become more mainstream, the banking sector is prioritizing customer-centric strategies, utilizing advancements that translate into tangible benefits for both the financial institution and its clientele. By leveraging AI to augment their processes, customer experience and product offerings, banks can personalize services, mitigate risks and streamline operations while boosting profitability and customer satisfaction.
In my experience, the main drivers for the implementation of AI-driven solutions in the banking sector fall into three primary categories: expanded revenue streams, enhanced customer experience and operational optimization.
Expanding Revenue Streams
Just-In-Time Marketing Efforts
By using AI to analyze and understand customer behavior, banks can enhance cross-selling and upselling opportunities. Through transaction patterns and customer interactions, AI can identify key life events, such as a marriage or home purchase, and trigger event-driven marketing, allowing banks to offer relevant financial products at just the right moment. This level of personalization—anticipating and proactively offering these products to the client based on their own lives—thereby increases the lifetime value of each customer while fostering brand loyalty.
Open Banking And API Integration
Many banks are collaborating with fintech companies, allowing third-party applications to connect to their systems via APIs. In fact, the number of open banking API calls is forecast to reach 580 billion annually by 2027. This approach not only broadens customer reach but also creates new financial models and revenue streams via partnerships. Offering open APIs and partnering with fintech and SaaS platforms can benefit existing clients and provide an expanded customer base for both the bank and the third-party platform. Some examples of these API integrations include connecting customer bank accounts directly to accounting software, buy-now, pay-later (BNPL) financing and digital lending platforms.
Enhancing Customer Experiences
Chatbots And Virtual Assistants
While chatbots and virtual assistants have existed almost as long as digital banking services, AI-powered chatbots provide increasing support to customers and are being deployed across numerous industries (even the United States federal government). However, while the chatbots of yesterday could only produce responses to frequently asked questions based on pre-programmed scripts, those powered by generative AI use natural language processing and machine learning to provide real-time assistance to customers.
It will, however, take time to train these models to be efficient in answering complex customer inquiries. To avoid client frustration as this training transpires, chatbots and virtual assistants will still need to offer customers a means of reaching a live customer service representative for questions beyond the scope of the model’s knowledge base.
Predictive Analytics And Personalized Banking
The predictive capabilities of AI allow banks to anticipate the needs of customers before they even arise. By analyzing transaction histories, spending behaviors and customer engagement patterns, banks can proactively offer relevant products and services. For example, AI can detect a customer’s frequent international travel and recommend solutions, such as fee-free foreign transactions or fraud prevention measures.
Predictive analytics can also provide AI-driven financial wellness tips and tools based on customer activity to aid bank clients in managing their funds more effectively. These insights allow the bank to provide customized insights into spending habits, assist with budgeting strategies and ultimately foster stronger financial health for their customers.
Optimizing Operations
Process Automation And Efficiency Gains
From loan underwriting to regulatory compliance reporting, using AI to automate workflows, reduce human errors and enhance accuracy provides increased operating efficiencies across bank departments. AI-automated reconciliation tools eliminate discrepancies that often plague the manual process of balancing the bank’s internal ledger. Similarly, using AI to aggregate data for compliance reporting may significantly reduce the risk of errors that can lead to regulatory penalties.
Increased Cybersecurity
As cyber threats grow increasingly more prevalent and sophisticated, engaging AI to detect and prevent fraudulent activity on client accounts and the bank’s technological infrastructure is becoming more essential. AI-driven cybersecurity systems continuously monitor transactions and suspicious activities, allowing the bank to mitigate fraud and unauthorized access in real time.
By nature, banks are inherently data-rich, processing massive amounts of consumer transaction data daily. Utilizing this data to train an AI model to recognize individual consumer spending patterns allows the AI to recognize and flag anomalies as suspected fraud. For example, if a customer typically spends $2,000 per month and a sudden $10,000 transaction hits the account, it would trigger an alert before processing the transaction. These proactive security measures not only protect customers but also increase client trust in the banking institution.
Overcoming Implementation Challenges
Many of the challenges the banking sector faces regarding the implementation of advanced AI adoption are similar to those I observed in my study of the implementation of digital banking in Mauritius in 2018. To remain competitive, decision-makers in the banking sector will need to find ways to overcome the following obstacles.
Data Silos In Legacy Frameworks
In my opinion, eliminating data silos is one of the most significant hurdles in implementing AI technologies in banking. Most financial institutions worldwide are operating within multiple legacy systems that do not communicate with each other. This makes it difficult to consolidate and analyze data holistically, even within a single bank. Overcoming this will entail significant investment into modern data integration frameworks facilitating data sharing across departments.
Maintaining Regulatory Compliance
Presently, regulations in the United States surrounding the use of AI in the banking sector are relatively fluid. As such, financial institutions will need to closely monitor regulation changes in order to stay compliant. Priority must be given to privacy and data protection laws as institutions gather and utilize customer data. The implementation of AI-specific legislation and regulation has already occurred with the European Union’s AI Act, which lays a framework for a risk-based approach to AI adoption.
At the end of the day, the bank should not lose focus; the implementation of AI solutions should be customer-centric. If the customer is happy, then the bank will generate more revenue.
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