The Banking Industry Fuels the Heart of Money with Artificial Intelligence


Big data analytics have had a tremendous impact on the banking industry.  Bank of America, Wells Fargo, BBVA, Citi Ventures, and Ally Bank are currently using machine learning programs. According to Accenture, 67 percent of banking employees expect intelligent technologies to create new opportunities for the nature of their future work.  Machine learning insights have provided competitive advantages that have been both lucrative and game-changing.  Executives can leverage their existing operating models to understand where to benefit from strategic artificial intelligence investments.  From fraud detection to customer service, the banking industry is thriving off of business intelligence. Andrew Lo, Director of the Laboratory for Financial Engineering at the MIT Sloan School of Management shares, “I suspect that it’s going to transform all aspects of the financial industry because there are so many parts of it that can be automated using these kinds of algorithms and access to large pools of data.”

Tata Consultancy Service research estimates that within banking industries that artificial intelligence has already helped them reduce costs by 13 percent. Given the breadth and depth of the digital revolution, executives should also involve those with machine learning expertise in strategic planning sessions in the boardroom. To maximize the return on big data investments, it is critical for executives to understand better the role that big data plays in each aspect of their business. If banking executives do not stay current on the big data advancements, it may result in lost market share. Specific areas where big data is being successfully used now in the financial services sector includes fraud detection, marketing, credit risk management, operational efficiency, and customer insight and service.

Fraud Detection


McAfee’s recent report estimates that cybercrime costs the global economy $600 billion, or .8% of global gross domestic product with the most common crime being credit card fraud.  Many of these frauds can happen very quickly. Fortunately, systems powered by machine learning are excellent at rapidly reviewing large volumes of data and detecting patterns and outliers that may indicate fraud. Different clusters can be modeled to fit various customers’ profiles and transactions to support predictive modeling embedded with machine learning.  Banks can use advanced algorithms to identify stolen credits cards and fraudulent purchases before the customer is even aware that there could be an issue. In 2016 alone, it is estimated that artificial intelligence algorithms sopped nearly $17 billion in fraud activity. As new algorithms become more sophisticated, neural networks have the potential to reduce future economic losses drastically.



Big data analytics allows transformative marketing in both personalization and research.  Mass email blasts are a tool of the past and will likely not be effective in upcoming years as customers are feeling overwhelmed by the amount of information they receive. New advanced machine algorithms support automated and real-time customer engagement and even content development when customers tweet, post on Facebook, upload a thought on Instagram or write a blog referencing the company. Machine learning can reach millions of targeted customers in seconds. Also, customer needs and values can be prioritized and matched with strategic communications to ensure the effectiveness of the message. Marketing with machine learning is moving from a “nice to have” tool to an integrated part of the company’s business strategy. Companies are also leveraging new machine learning techniques to study consumers online to inform their brand loyalty and trust strategies better.

Credit Risk Management


Over the years, banks have faced increasing scrutiny as it relates to credit standards and poor risk management strategies. Generally speaking, credit risk is when a potential borrower does not meet the lending obligations and terms. Banks want to maximize their risk adjust the rate of return and balance the risk in their portfolios. According to a survey by the Global Association of Risk Professionals, 88 percent of bank executives think machine learning adoption could provide a foundational change for their risk management programs.

With risk and value being different sides of the same coin, it is not surprising that according to the Syncsort survey, more than half of respondents said they already use big data tools to increase revenue and accelerate growth. Banking data is starting to be combined with personal data to improve forecasting for credit card risk management.

Simudyne Technology has a tailored approach in providing banks with computer models that show millions of future scenarios and lets the executives test how individual facts interact in those different scenarios. Other machine learning techniques include leveraging models that incorporate the individual size of the bank and risk tolerance levels to drive new operational processes. For example, some banks are using outstanding customer balances or previous payments and then overlay that data with standard credit bureaus to create data sets to better access individual risk within each data transaction. Banks can leverage machine learning in credit risk portfolios to then raise or lower an individual credit line as appropriate in a way that is in alignment with the bank’s risk tolerance level. As regulation continues to increase, new algorithms will likely be created to determine better the levels of risk involved in the exchange of value, payment and settlement disputes, as well as risk-return policies.

Operational Efficiency


According to the Synscort report, over 40 percent of respondents said one of the most significant benefits of big data is the ability to increase business agility. Not surprising that many businesses are using big analytics to improve operations. Machine learning provides the ability to process more transactions while eliminating downtime. For example, Bank of America is already using voice commands to look up account information and transfer money. Robotics Process Automation is automating processes by augmenting human capabilities.  And, Union Bank has already deployed RBA bots that automate ATM transactions. Machine learning is also being used to support more timely decisions on customer loans. The traditional bricks and mortar strategy of banks can be supplemented, if not replaced shortly by user-friendly apps and online solutions.

Customer Insight and Service


Since banking institutions already have to be transparent with their data as a result of the General Data Protection Regulation and Payment Services Directive, why wouldn’t these organizations also leverage that same data for competitive insight about their customers to grow their business? According to a NewVantage survey, 53.4 percent of companies have experienced success in improving customer service as a result of their big data analytics program. As financial institutions learn more about their customers with each transaction, there is also the expectation of more personalized customer interaction. Customer expectations have changed as they expect increased insight from the company.

For example, artificial intelligence can be leveraged to respond to commonly asked questions in a timely fashion. Banks are also looking towards machine learning to advise customers better about proper investments and savings recommendations based on their transaction history. According to Matt Meyer, CIO for BrightStar, “It’s about keeping pace with other industries. Ride-sharing apps have been a disruptor to taxi services, and there are lessons to be learned there for the financial industry. Customers want ease of access, they want ease of payment, and they want that speed.”

In summary, sustained investment in big data is critical to keep the financial services industry relevant. In a recent study by Accenture, 77 percent of banks plan to use artificial intelligence to automate tasks in the next three years significantly. “We’ve been working to build out the technology stack at Citi so that we can drive broad adoption of machine learning within various use cases and functions,” said the Managing Director of City Ventures.  Frankly, with all the major players in banking at the table with machine learning, it is no longer a luxury but rather an operational necessity. Those that engage digital innovation experts in their strategic planning sessions in the boardroom are likely to be better prepared for this digital revolution.

Autonomous Next conducted a recent study that showed potential costs savings due to ongoing machine learning programs could result in cost savings of $450 billion across the banking industry by 2030. The evolving digital economy creates new opportunities and threats for traditional banking systems in a variety of areas across their core business. Addressing the existing high-priority banking problems with artificial intelligence and machine learning will not only keep banking organizations relevant but in a time of increased economic concern, if embraced strategically, could help pave their way to market dominance.

#AI #BankingIndustry #MachineLearning

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