For some time now, artificial intelligence has been transforming banking as we know it – and, no, I’m not just referring to chatbots used for engaging online banking customers. Chatbot solutions are based on a type of AI known as Natural Language Processing (NLP), which is just one of many forms of Machine Learning impacting banking.
According to Narrative Science and the National Business Research Institute, today “32% of financial services executives confirm using AI technologies, such as predictive analytics, recommendation engines, voice recognition, and response.” In this blog post, I’ll discuss some of the various ways AI is being applied to finance and banking to improve efficiency, drive security, and maximize business performance for financial institutions worldwide.
CCTV security and video surveillance have long been a valued resource for solving and preventing crime in diverse business settings. For banks, video surveillance is used for monitoring individual public branches, national corporate headquarters and critical infrastructure buildings as well as their perimeters, entryways, parking lots, ATM terminals, and more.
While most banks rely on video surveillance for security, they face many challenges with traditional surveillance when it comes to investigating and preventing crime. For one, manually identifying suspects in video can be difficult and time-consuming, especially when (and herein lies the other challenge) there are hours of footage to review to understand the incident and pinpoint a suspect.
Artificial intelligence-backed video analysis software enables banks to overcome these and other challenges. Utilizing Deep Learning techniques, video content analytics solutions analyze video, identify objects that appear and then extract and classify them, indexing the video metadata to make video searchable, actionable and quantifiable. With video analytic capabilities, banks can:
A centralized financial institution, such as the corporate headquarters of a bank, could aggregate the video data across its bank branches to understand broader behavioral trends in its banks, in order to optimize customer service, create more secure building layouts, generate better security protocols, and more. For major financial institutions, these capabilities can save hundreds of thousands of man-hours each year and considerably improve loss prevention.
Recently, personal finance management (PFM) AI has seen some game-changing advancements. One powerful application of AI-driven PFM is 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. Opportunistic forecasting increases the likelihood of spotting and exploiting unique opportunities. The number of benefits of accurate forecasting is limited only to the imagination. On the whole, it comes down to improvements in the accuracy of predictions.
Prediction accuracy soars when more client spending data is aggregated 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.
According to Forbes, the costs of cybercrime are estimated to be about 0.8% of annual global GDP, with credit card fraud as a leading – and growing – problem. While cybersecurity is a multifaceted and broad discipline, a critical niche where AI can have a major impact is fraud detection. As noted by Forbes, “The speed at which financial losses can occur when credit card fraud takes place makes intelligent fraud detection techniques increasingly important.”
Fraud detection has long been a sensitive issue, with accuracy – or lack thereof – presenting major challenges: Too often customers accidentally trigger anti-fraud measures when no fraud has occurred. For instance, it’s not uncommon for clients to be locked out of their accounts based on a false alarm – resulting in frustration and potentially lost business for banks and finance apps. While ensuring customer data security is a first priority, accurate fraud detection is a close second. Artificial intelligence is being leveraged to reduce false alarms and increase accurate fraud detection. More and more, banks are relying on business intelligence AI to drive productivity and detect fraud accurately.
The possibilities for AI in banking go far beyond fraud detection. AI applications offer numerous financial institution security and performance benefits. Possible applications include anti-money laundering, algorithmic trading, and much more.
An enormous subcategory of financial services, financial advice, has – until now – relied heavily on speculation. As Fidelity Institutional Vice President, Andrew Brzezinsk, notes: “AI [is] a rapidly emerging, and strong opportunity capability: We could improve interactions with our advisory business clients and help them be better advisors to investor clients.” With access to data and the artificial intelligence applications to process and visualize it, banks and finance solutions can deliver valuable analysis of otherwise underutilized data, offering customers clearer analytics-based advice for financial decision-making.
AI-driven financial analysis is also indispensable for financial advisors – and its importance will continue to evolve as the technology develops – enabling those with the most advanced systems and the deepest data resources to far outperform the competition.
AI, like many tools, is a force multiplier – expanding the reach of the operator, enhancing his or her capabilities, and broadening the user’s perspective. As AI technology advances, enhanced perception, cognition, attention, and logic will enable solutions and empower businesses in ways that have never before existed. Whether for banks, financial institutions or any other type of organization, AI is revolutionizing the ways we do business.
Signup to receive a monthly blog digest.