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Artificial Intelligence Use Cases in Banking

Artificial Intelligence Use Cases in Banking

Industries are finding a need to redefine themselves as digital disruption takes hold. Technology is driving the modern world, and every industry must find a way of creating values that tech-savvy customers demand. The rise and investment in Fintech in the last decade put the banking industry towards the top of the list when it comes to investment in new technologies. A steady increase in competition means traditional banks cannot rest on old processes and legacy systems.

Banks are using artificial intelligence (AI) as both an analytical solution and as a way to serve customers better and handle internal functions. AI works as a remedy for many challenges that the banking sector face, such as fraud, customer experience, security, operations, and financial forecasting.

In this article, we look at some of how the banking sector uses AI to improve efficiency, service, and productivity, as well as reducing costs and improving return on investment.

What is AI?

Broadly speaking, AI uses machines to perform complex tasks that only human intelligence can resolve. CEO of Google Deep Mind, Demis Hassabis, says it is the “science of making machines smart.” An intelligent machine is one that can perceive its environment and optimize its success at achieving a goal. A computer with AI capabilities can ingest data, analyze it, understand it, takes actions from it, and improve its performance independently.

In their book, “Artificial Intelligence: A Modern Approach,” Russell and Norvig say an intelligent machine can sense, comprehend, act and learn continuously and incrementally.

AI can impact every part of the banking value chain. The heat map below from Accenture shows the potential scale of applications across a multi-functional enterprise.

banking value chain

Source: Accenture.com

With so many applications, you could probably write an entire whitepaper on how AI transforms banking. However, for this article, we will keep in brief and focus on the most common and critical areas of AI that everyone needs to understand.

Artificial Intelligence in banking

Banks that are most likely to benefit from AI are those that can rethink approaches to people and processes. There is a need to innovate at scale and pace, requiring humans and AI to drive operational and process efficiencies. Applications of AI will generate growth through both customer and employee experiences.

The AI in Financial Services global study reveals that 85% of all respondents currently use some form of AI to boost speed and efficiency, with 77% saying it is one of their most important investment areas going forwards.

Chatbots

A chatbot or digital personal assistant is an AI software that allows users to communicate with a system interactively.Chatbots can chat with users in natural language using deep learning algorithms that translate conversations using a rule-based approach.In banking, chatbots create an interactive experience while reducing simple queries that a customer needs to refer to a human agent. The customers get a satisfying digital experience, while banks can reduce their costs. Moreover, a chatbot can operate 24 hours per day, without breaks, making it a highly efficient member of the workforce. Customers can reach out for service whenever they need it.

Erica is a virtual assistant from the Bank of America. Artificial intelligence chatbot can handle tasks like card security updates and credit card debt reduction. During 2019, Erica managed over 50 million client requests, rather than requiring human agents. The American Express Amex Bot provides customers with on-demand interactions to answer account and card queries.

It is important to note that chatbots are not here to replace human workers. The objective of the technology is to facilitate manual and repetitive tasks, allowing employees to focus on more complex and strategic projects.

AI-enhanced security

With the amount of personal and confidential data in the industry, it is impossible to under-emphasize how vital security is to banking. Banks have a responsibility to keep their clients’ money and personal information safe.

The digital age opens up new routes for cybercriminals. Instead of visiting a branch to commit a crime, everything can operate remotely, making them hard to track and stop.It becomes imperative that banks enhance their security as much as they can,primarily digital security. AI provides what most people refer to as next-generation security,AI-based systems nearly impossible to hack. The rise in cybercrime has prompted banks to strengthen their digital security infrastructure and to use AI, and they can create hypothetically unhackable systems. Any unhackable system today can become hackable tomorrow.

AI-enhanced security measures can also help prevent fraud as they enable bank systems to flag suspicious transactions before they are completed quickly.Datavisor uses machine learning algorithms to counteract application and transaction fraud in real-time. They promote a 94% fraud detection rate with leading US banks among their clients.

Biometrics such as fingerprints, iris, and voice recognition are now prevalent within banking for better security. Biometric characteristics are nearly impossible to forge, making them the perfect replacement for traditional banking passwords and PINs. Caixa Bank in Spain claims to be the first to allow customers to withdraw money from an ATM using facial recognition.

Lloyds Bank realizes the growth in voice search as a growing number of people own assistants such as Alexa, Google Home, or Siri. Lloyds uses voice biometrics to confirm identity using an analysis of voice characteristics. There are concerns around voice safety as those looking to do malicious damage could use recordings, so users must be careful to use them in privacy.

Compliance

Regulation plays a significant role in the banking system. AI can help by facilitating complex analysis of data, automating manual compliance processes such as “Know Your Customer” (KYC), and “Anti-Money Laundering”(AML). Both of these processes rely on gathering data from various systems to understand customer and transactional behaviors. Without AI, it can be very time-consuming, delaying the service offering to the end-user and at a cost to the institution.

AI algorithms can quickly integrate data from multiple systems in real-time, accurately, and efficiently. Using various sets of rules, the machine learning models can investigate patterns in behavior, ascertaining if there is any likely risk to the bank. A human could take days or weeks to complete a task that a machine does in seconds.

Studies show that 51% of financial institutions regulate KYC and AML manually. It is essential that banks invest more in compliance procedures to free up resource time and better allocate costs.

Financial Forecasting

Banks make a profit through the interest they earn on loans. However, that is under the assumption that people pay the money back and don’t default.

Machine learning algorithms analyze millions of data points in real-time, assessing whether a customer is an acceptable risk before coming up with a decision. ZestFinance, otherwise known as Zest AI, has an AI-powered underwriting solution to help companies assess borrowers when they have limited credit histories.

Zet AI uses thousands of data points, allowing them to consider audiences that would generally be high-risk. Auto lenders that use the system have cut losses by 23% annually, according to the Zest AI website.

Similarly, Underwrite.ai uses machine learning to analyze thousands of data points from credit bureau sources. They claim the platform can reduce defaults by as much as 50%, which is backed up by one of their clients, who is a high-profile market lender.

Over time, the forecasting algorithms will learn from experience. Machine learning needs quality data, and lots of it to operate efficiently. As the models make decisions, they learn from those and become more accurate the time. In the next few years, we should find that incredibly precise frameworks are in place for lending.

Minimizing operational costs

AI in banking can eliminate the errors that you might associate with human manual processes like paperwork and data entry. Automation bots (RPA), AI assistants, and computer vision can simplify human tasks using techniques like process mining and discovery. According to Accenture, AI can help banks to reduce costs by up to 25%.

AI tools gather data, classify, and action it without any human intervention. For example, they could scan an email inbox for invoices, find the relevant text in the data, input the text into a system, review the information, and then make some decision. AI-powered machines will allow humans to spend time on more creative, high-value tasks, will computers take care of anything manual and repetitive. The result is more satisfied customers and staff.

Summary

AI is not the future of banking; it is the present. As more data becomes available and new technology like quantum, edge, and cloud computing continues to transform the market, banking is going through something of a revolution. All financial institutions must invest in AI solutions now. The days of waiting to see how consumers respond are behind us. People want novel experiences and excellent services from every service they use. If their banks cannot provide that, there are plenty of exciting fintech solutions that can.

Banks need to pull together people, processes, and data, having them all working collaboratively. In a new era of AI-driven enterprises, the financial industry will embrace its transformational power.

 

Avinash Misra is the co-founder and CEO of Skan.AI, a cognitive process discovery and operational intelligence platform.

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