In today’s rapidly evolving financial landscape, artificial intelligence (AI) is no longer a futuristic concept but a present reality reshaping the banking industry. From enhancing security measures to optimizing customer experiences, AI is driving increased efficiency and business performance across the sector. Let’s explore four AI applications that are making a significant impact for banking organizations.
AI-backed video analytics is taking security surveillance to new heights. While video surveillance has long been a cornerstone of bank security, traditional surveillance presents many challenges for investigating and preventing crime. For instance, manual suspect identification is time-consuming and prone to error. Video analytics software enables banks to overcome these and other challenges by utilizing AI and Deep Learning to analyze video, identify objects, and then extract and classify them, essentially making video searchable, actionable, and quantifiable. What was once passive footage, transforms into critical intelligence.
With video analytics, banks can:
While security remains a primary application, video analytics is proving to be a versatile tool to support optimization across every area of the banking organization. For instance, the corporate headquarters of a bank, could aggregate video data from all its bank branches to understand broader behavioral trends in its banks. The data can then drive strategic decision-making to optimize building layouts, direct better guest services, and even generate better security protocols. For major financial institutions, these capabilities can save hundreds of thousands of man-hours each year and considerably improve loss prevention. Check out these “Beyond Security” video analytics use cases for banking organizations:
According to a 2018 article from Forbes, the costs of cybercrime was estimated to be about 0.8% of annual global GDP, with credit card fraud as a leading – and growing – problem. Fast forward to 2024 and data from SPD Technology, in their report on “Credit Card Fraud Detection Using Machine Learning”, confirms that globally, credit card fraud causes over $30 billion in annual losses. While cybersecurity is a multifaceted and broad discipline, fraud detection is a critical niche where AI in banking can have a major impact.
However, the fraud detection of the past was riddled with accuracy issues, causing significant user frustration and loss of business for banks. How many times have you yourself accidentally triggered anti-fraud measures when no fraud has occurred? How did you feel about getting locked out of your account? Unpleasant right? Now, as artificial intelligence has become more sophisticated, false alarms have been reduced, providing more accurate fraud detection.
AI applications for fraud detection can be used to:
By processing vast amounts of data and identifying subtle anomalies, AI-driven systems provide a robust defense against financial crimes that protect both banks and their customers.
Personal finance management has also seen some game-changing advancements with the integration of AI in banking. According to The AI Journal, “AI-driven applications can automate budgeting, track expenses, and provide real-time financial insights, helping individuals make informed decisions.”
AI-is particularly helpful with spending forecasts, which leverage a user’s personal spending data to generate reliable spending predictions over a given period. The benefits of spending forecasts are straightforward: improved risk analysis lets clients and organizations make better decisions, and opportunistic forecasting increases the likelihood of spotting and exploiting unique opportunities.
But what about accuracy? Prediction accuracy soars when the client provides greater quantities of spending data and when the prediction window is limited. But, as the technology becomes more sophisticated, predictive analytics will be able to deliver more precise forecasts for longer time periods. AI-driven personal finance management tools empower customers to make better financial choices while enabling banks to offer more tailored services, enhancing customer satisfaction and loyalty.
An enormous subcategory of financial services, financial advice, has – until now – relied heavily on speculation. However, the integration of AI in banking presents two strong opportunity capabilities: one, improve interactions with advisory business clients and two, help financial professionals be better advisors to investor clients. By pairing access to data with artificial intelligence applications that can process and visualize it, banks can deliver valuable analysis of otherwise underutilized data, offering customers clearer analytics-based advice for financial decision-making. Again, AI is not replacing human advisors but augmenting their capabilities so they can:
This AI-human collaboration results in more informed, data-driven financial advice, benefiting both clients and financial institutions.
For banking professionals, embracing AI technologies is no longer optional—it’s imperative for staying competitive in a digital financial world. AI is a force multiplier that enhances user capabilities. Therefore, by harnessing the power of AI across multiple facets of their operations, banks can transform challenges into opportunities that empower a more efficient, secure, and customer-centric banking experience for customers and employees alike.
Interested in supporting your banking organization with video analytics intelligence? Check out this report and learn how video analytics is delivering rapid ROI for banking and other industries worldwide. Get the report.
Editor’s note: This post was originally published in August 2019, and has been refreshed and updated for accuracy.
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