How AI is Changing Banking
The banking sector is tackling today's complex challenges with the help of AI, which is streamlining operations, enhancing security, and focusing more on customers. However, implementing AI can be challenging, as many banks struggle to identify exactly how they can use AI to achieve measurable results.
"A common area banks struggle with is identifying the best use cases for AI," writes SAP in an article for Forbes. "Start by looking at areas that offer quantifiable business value, such as revenue enablement, cost reduction or risk mitigation. Middle and back-office functions are ideal for this.
This article explores AI's significant role in finance, including the many use cases of this technology and how banks can achieve results.
AI Quick Wins in Banking
From enabling predictive analytics that combat fraud to providing personalized financial insights for every customer, artificial intelligence in banking is as diverse as the services it supports. It doesn't just optimize existing processes but catalyzes innovation, propelling financial organizations towards intelligent banking solutions that anticipate and meet the evolving needs of users.
It holds the key to enhancing operations, scaling personalized services, and mitigating the burgeoning risks of a digital-first world.
The question is, where should banks start?
AI Chatbots
According to a report by S&P Global, "Most AI use cases in banking have aimed to either automate tasks or generate predictions. This work has been done by supervised and unsupervised machine learning (ML) models (and sometimes more complex deep learning models) that require significant computing capacity, and large amounts of data."
Large banks may have the computing capacity to host complex ML models, but most banks don't have the time or resources to construct their own AI systems.
One of the quickest and most impactful ways to leverage AI is through chatbots—virtual assistants that can handle customer queries, provide personalized assistance, and even perform simple transactions. With sophisticated natural language processing (NLP) at their core, these chatbots offer a conversational banking experience that delights customers and reduces the strain on human customer service representatives.
Such chatbots can be built in-house, but a range of third-party chatbot applications can already be attached to a knowledge base to provide turnkey assistance. Through chatbots, banks can deliver 24/7 service, improve response times, and gather data on customer preferences and issues that can inform better service in the future.
AI Voice Assistants
Voice-activated AI systems are another accessible win for banks as they pivot towards AI. The integration of AI voice assistants redefines the concept of hands-free banking.
Once implemented, customers can perform a range of standard banking tasks, from checking their balance inquiries to paying bills, simply by speaking to their devices.
Fraud Detection and Prevention
Another area where AI can provide immediate benefits in banking is fraud detection and prevention. By analyzing patterns and trends in vast amounts of transaction data, AI algorithms can identify suspicious activities that deviate from a customer's typical behavior, flagging them for review.
This proactive approach not only reduces the incidence of fraud but also helps in building customer trust by ensuring their financial assets are protected.
Additionally, AI-powered systems can adapt and learn from new fraud tactics, continually enhancing their effectiveness over time.
Risk Assessment and Management
AI applications extend into the realm of risk assessment and management, offering banks a powerful tool to predict loan defaults, evaluate creditworthiness, and optimize financial portfolios. By leveraging machine learning models, banks can analyze historical data and market trends to make informed decisions, minimizing financial risks.
This capability is especially crucial in the lending process, where AI can analyze a potential borrower's credit history, income, and other factors more quickly and accurately than traditional methods, leading to faster and more reliable loan approvals.
Automation and RPA in the Back Office
Automating the tedious and manual tasks that clog up the back-office operations of banking is another sector ripe for AI disruption. Robotic Process Automation (RPA) is AI's application in automating mundane tasks, which has characterized the banking back office for decades. Today, RPA promises to replace these tasks with more efficient and precise automated processes.
AI-driven automation stands to revolutionize every aspect of banking, from loan processing to data entry, cutting down on human error, and significantly boosting productivity. The beauty of this technology lies in its adaptability: RPA can be designed to complement existing systems and processes, learning and evolving as it works.
However, the implementation of RPA requires careful planning. Successful integration should begin by identifying the areas that will benefit most from automation, ensuring that the technology can scale with the bank’s growth and that it complies with regulatory standards that govern the industry.
Example: AI in Mortgage Processing
A notable example of RPA's potential in banking is mortgage processing. By automating document verification, income assessment, and other routine tasks associated with mortgage applications, banks can significantly speed up the lending process, reduce errors, and free up human employees to focus on more complex and value-added tasks.
The implementation of RPA in mortgage processing not only streamlines operations but also leads to faster decision times, improving customer satisfaction and overall loan experience.
Generative AI Applications in the Financial Sector
Generative AI is not just about enhancing customer experience; it's about using data to inform decisions and strategies at a level of sophistication never before possible. A generative AI model like ChatGPT can assist banks in creating compliance documents, reports, and other documents with minimal human intervention.
Overcoming Challenges in Generative AI Adoption
While the potential of generative AI in banking is vast, its adoption is not without challenges. These models require large datasets, high computational power, and sophisticated expertise to train and operate. Most banks would benefit by using a third-party solution, as this would enable them to gain the benefits of generative AI without the infrastructure footprint.
There's also the question of responsible AI usage, particularly when it comes to generating content. Banks must create a process for checking generated content for errors, inconsistencies, or erroneous information before it is released, either internally or externally.
Embrace AI in Banking
For banking leaders, the time is now to begin the AI conversation within the organization. By starting small with manageable AI deployments, learning from those experiences, and gradually scaling up, banks can integrate AI into their DNA, remaining at the forefront of an industry that is undergoing an exciting transformation.
To learn more about what your financial institutions can do with AI, don't miss Future Branches 2024. It's happening from June 24 - 25 at the Westin Copley Place in Boston, Massachusetts.
Download the agenda and register for the event today.