Today, artificial intelligence (AI) is being utilized in a wide range of industries for several functions, including handling administrative duties, assisting customers with problems, and making predictions and recommendations based on experience.
Nowadays, banks provide digital platforms for most of their banking transactions: mobile & web applications, ATMs, etc. As a result, the majority of transactions are self-served by customers. By combining data analytics and artificial intelligence, the banking and finance sector can offer their customers a truly personalized, omnichannel experience.
This blog will examine how banks are empowering their customers, as well as provide an overview of how AI has the potential to give a bank a competitive edge.
The Positive Outcomes of Incorporating AI With Banking & Finance
In the world of banking and finance, there are some great benefits to AI.
Leveraging AI to Reduce the Operational Costs
Banking is often digital, but it still has many human-based operations, which can be paperwork-heavy. Since there could be human error, this can result in significant operational costs and risk issues.
Robotic Process Automation or RPA has been introduced in the banking industry to automate customer data entry. The incorporation of this software can perform tasks the same way a human would, making inputting data in much easier way.
With improved AI, RPA bots are now capable of handling increasingly complex banking workflows, such as those handled by humans before.
Introducing AI-Chatbot to Banking Processes
Baking AI chatbots have helped the BFSI industry by reducing the resolution time, which further aids in optimizing operational efficiency, and costs, along with enhancing customer experiences.
Of late, digital transformation has led to an increase in contactless and paperless banking services, improving efficiency, productivity, and customer satisfaction rates.
Leading Indian banks are nowadays leveraging the full potential of AI via chatbots to enhance their customer experiences.
With an AI chatbot, banks can rest assured of saving costs, while generating a high ROI. Also, AI-chatbots can effectively (use the least amount of time) handle the most commonly accessed tasks, like managing mini-statements, balance inquiries, and fund transfers, which results in reducing loads on other channels, like banking mobile & web applications, internet banking, etc.
Advancing Fraud Detection & Regulatory Compliance Methods With AI/ML
Banks that want to better protect their customers from cyber fraud should utilize real-time fraud detection and prevention solutions, which are AI-based. These advanced methods are way more efficient than the conventional rules-based systems, which can only recognize “known” fraud scenarios.
However, these advanced rules are sometimes responsible for generating a high number of false positives – leaving the customers dissatisfied with fraudulent transactions either being unable to detect or blocking their transactions’ limits. Here machine learning (ML) comes into action.
ML allows banks to figure out their customer’s normal behavior and use behavioral risk models. This will help the banks detect more fraud in less time with fewer false positives, which leaves their customers highly satisfied.
Tailor-made Banking Experiences for Customers
Instead of carpet-bombing clients with substandard banking offers, AI can personalize any offer catering to a customer’s individual needs. Based on the customers’ current financial activity, AI can offer a matching interest rate to them. Additionally, when clients accidentally overdraw their checking account balance, an AI can decide to waive the fee automatically, if it reflects a key customer.
Implementing Robo Advisory to Transform Banking Customer Service
One of the most controversial topics of automation in finance is Robo-advice. The Robo-adviser reviews the customer’s financial data and then suggests a portfolio based on their needs, which could be as specific as a specific product.
Moreover, Robo-advisors help customers invest by analyzing data about their financial health & history, and industry expertise to recommend appropriate investments.
Impacting Fintech’s Cybersecurity
With the use of AI, data can be captured and used to improve cybersecurity. It has the ability to predict future threats as well as prevent any possible breaches, external threats, or cyberattacks, which in turn can help stop data theft or abuse. Thus, with AI incorporation, cybersecurity in banking systems will not only be alerted to future breaches but to proposed “corrective actions” as well.
Automating Administrative Tasks with AI
Financial institutions can leverage AI to make a more personal connection with customers. With AI automatically handling many of the routine tasks, banking employees can solely focus on building meaningful relationships with new and existing customers, along with finding creative solutions to any problems they may have.
Risks Associated with AI in Banking & Finance
Emerging technologies are risky because they are still in the experimental stage. The risks of emerging tech are compounded by the fact that the industry is evolving quickly. Therefore, along with all the AI benefits, banking & finance must consider the downsides.
Bias Around AI
One of the problems with AI is that there are biases in how people train the machines. For example, a person might teach the machine a certain assumption about what a good house looks like. These prejudices can be even worse when brought to life.
Explainability
It is difficult to deploy tools that use deep neural networks because they work by figuring out correlations between large quantities of data. Although deep learning neural networks are extremely useful, however, it is difficult to implement because they require an explanation to the customer.
Reliability
While complying with regulations, banking services should prioritize earning customers’ trust before implementing AI tools. Humans enjoy the convenience of chatbots, but these bots will lose their trustworthiness if they make mistakes.
Cost Efficiency
New AI is usually difficult to run, because it costs more money than most companies can afford to spend. However, most experimental algorithms are cost-prohibitive to use.
The Next Step
AI is transforming the entire process of automation in that it’s intelligent enough to bypass any digital risks and challenges from the BFSI sector. Nowadays, most banks rely on AI for all processes, with this technology always innovating and balancing human capabilities without much manual intervention.
With these investments in AI, leaders in the banking & finance sector have already achieved operational and cost efficiency, along with offering personalized services to their customers. All these benefits will no longer be just a futuristic vision; if you invest today with due diligence in AI to taste automation in your banking processes.
