The banking sector has witnessed multiple paradigm shifts, traditionally occurring in parallel with emergent technology trends. Technologies that were previously at the forefront of banking transformations are now standard issue by modern banks, with the most notable examples taken for granted by millions of customers on a daily basis.

The advent of credit cards and magnetic strip technology arrived during the 1960’s and reached mass adoption during the 1970’s. At the same time, the first ATM was installed in 1967 in London, easing customers’ access to their funds, and simplifying back office processes.

However, the most major transformation to date was the introduction of online banking. By 2006, over 80% of banks had adopted online banking, revolutionizing customer banking access and providing significant cost savings for banks.

But the biggest revolution in banking technology is happening right now–the emergence of artificial intelligence (AI) and big data.

During 2019, the combined power of these two technologies is expected to transform three major areas of banking operations. We examine which parts of the banking value chain those changes are most likely to impact.

Marketing and Sales

Big data sets offer unique opportunities for banks, but processing such an enormity of data is not without its difficulties.

The most significant development in this field is AI-driven predictive analytics, developed from prediction sciences. Predictive analytics processes customers’ data via AI and machine learning, to identify data which offers insight into future events.

Advancements in predictive analytics are expected to be a boon for banking marketing departments, which hire big data scientists to manually clean, analyze and draw predictions from big data sets. This process is costly. So costly, that by 2020 big data analysis will cost $203 billion, with banking accounting for over 13% based on previous years.

In addition to expensive salaries and scarcity, data scientists may also take months to gather useable results. That’s where AI-based models come in. Data science firms are entering the market, offering AI-powered solutions with greater efficiency than traditional data scientists, offering answers in minutes rather than months.

One such predictions platform, Endor, is based on “Social Physics” theory from MIT, a novel research area focused on prediction of mass human behavior. Using field-tested models, Endor allows banks to ask their platform direct questions, aimed at predicting customers’ behavior–such as “Out of all active card holders in the last 6 months, who is likely to stop using our card next month?”.

AI has the potential to redefine many of the tools and methods we use for analysis, learning, and interpreting the world around us, said Yaniv Altshuler, founder and CEO of Endor.

“The technology is already gaining traction in the business community where experts predict that it will be a driving force over the next decade. From a mathematical point of view, it is likely that commercial AI machines will be exposed to vastly more information than any professional trader can possible digest, giving a clear ‘win’ to AI and ML models on the ‘data’ aspect.”, adds Altshuler.

AI-powered predictive analytics provides significant cost and time savings for marketing departments, and boosts customer loyalty, by only contacting customers who are most likely to be receptive to new services.

Risk Management and Fraud Prevention

In the fight against fraud, few technology solutions hold as much potential as AI-driven big data. As online banking customers become more savvy, 80% of customers agree that fraud protection measures are a priority when choosing a banking service.

Banks can utilize AI and machine learning to track how customers use their accounts. In a recent high profile case, AI technology revealed fraudsters accessing a victim’s bank account, by identifying that they were using the scroll bar to navigate the site–while the victim accessed their account through their laptop and only used a trackpad.

As extreme as this example is, it’s a testament to the power of AI-based fraud detection techniques, which are able to store and process huge data sets, tailored to individual customer behaviors.

Risk management will likewise benefit from AI learning. It’s estimated that banks could realize a 25-50% fall in credit decision times, alongside a 10% reduction in credit losses, with AI-based models.

Featurespace, a provider of AI behavioral analytics for risk management, detects and blocks banking fraud attacks in real-time. Their proprietary platform, called ARIC, can result in a 70% reduction in declined transactions, but with a 50% improvement in operational efficiency; simultaneously preventing fraud while approving genuine new customers.

By removing the potential for human error while approving a loan, banks can avoid fraudulent borrowers, or those with poor credit scores, vastly increasing their revenues and improving customer service.

AI Chat Bots and Robot Operators

Existing within the realms of science fiction just a few decades ago, modern AI-driven chat bots and operators are increasing their capacity for meaningful contributions to customer service, operating as the public face of online banking.

Although their ancestors were a source of frustration, modern robot phone operators are utilizing AI emotional analytics to monitor customers’ voices, identifying and developing unique algorithms to changes in tone and negative responses. Not only is this data used to improve customer experience, but similarly, robot operators can flag potentially high-risk interactions.

Moreover, chat bots are important for online banking. In addition to cost savings compared to human equivalents, chat bots are also able to offer financial advice and 24/7 customer support.

Kasisto, the leading enterprise-ready AI platform for conversational chat bot banking, resolves 82% of all conversations with real customers without the need for human assistance. The AI chat bot, called KAI, can assist customers in locating transactions, making payments, and onboarding new account holders.

In a recent survey, 65% of banking firms believed that customer service, including marketing and the use of chat bots, was the component of the banking value chain which would be most significantly impacted by AI.

Banking on AI and Big Data

With a multitude of benefits, it’s little wonder that financial providers are banking on AI solutions. With a surge in AI technology applications during the last five years, banks are optimizing their operations across the banking value chain using AI-based customer service, fraud protection and marketing.

Although the AI banking transformation may take many years to implement industry-wide, 2019 looks to be a landmark year for big data analysis and AI adoption in the financial sector.

Produced in association with StudioWorks