For financial services, minimizing risk exposure is a survival imperative. Only AI-based technologies can provide the coverage, speed and accuracy that today’s customers – and possible risks – demand.
Leveraging their robust technology, compliance, operations, and banking infrastructure, financial organizations deliver a suite of services that range from digital payments, banking, insurance, cross-border transfers, foreign currency, to trading and more. With billions of daily financial transactions happening on their platforms 24/7, fintechs closely monitor hundreds of thousands of performance metrics in every area across the business. They are watching for any indication that something is even slightly off kilter with the business—doing everything they can to mitigate the risks of fraudulent activity, suspicious transactions, service degradation, customer experience and compliance issues. The sooner an issue can be detected and addressed, the stronger the business can be.
For financial services, minimizing risk exposure is crucial for survival. Their success is predicated on operational efficiencies and on the consumer perception that they are completely reliable. But where money is concerned, risk comes in many flavors, including security and compliance, operations, revenue optimization and the customer experience. When incidents or issues aren’t quickly identified and resolved, security breaches, operational inefficiencies, revenue leaks and customer churn take an increasing toll on the business.
For these reasons, the last year has seen a significant rise in AI adoption by the financial sector. In fact, financial services are currently in the midst of a profound technological transformation. Customer expectations, technological capabilities, regulatory requirements, demographics and economics are together creating an imperative to change. In a competitive environment of rising cost pressures, where rapid response and action are critical, fintechs have no choice but to modernize their technology and digitization of both the front and back ends of their businesses.
The name of the game here is AI-based technologies. According to McKinsey, to successfully compete and thrive financial services must adopt AI technologies as the foundation for new value propositions and distinctive customer experiences. Doing so could potentially unlock $1 trillion of incremental value for banks annually.
Fintechs typically have millions of customers across the globe and must manage millions of daily business metrics involving transactions, withdrawals, deposits, wire transfers, APIs, log-ins and payment gateways, among others. Every transaction, click, purchase, etc. generates a data point, that together form a vast number of data streams.Traditional manual monitoring of this data causes significant delays of at least 24 hours or longer in detecting and resolving critical incidents, which threaten to impact customer satisfaction, brand equity and the company’s bottom line. Human-centric approaches like dashboards and static thresholds are not scalable, efficient or cost-effective enough to meet this challenge.
This is where AI- and especially Machine Learning-based technologies are creating a qualitative change for financial institutions. They do so by automatically learning the data’s normal behavior, including seasonal and other complex patterns, to identify and alert stakeholders on any combination of metrics that behave abnormally. AI technology provides financial institutions with the tools needed to detect and diagnose issues early, resolve them quickly, and take preemptive actions before they turn into crises. This is a drastic change from the static nature of BI as it exists today, which is quickly translated into positive impact on the businesses’ bottom line, as predicted by McKinsey.
Faster, cheaper and more customer-focused
Take for example a banking institution experiencing a significant drop in the expected number of settled payment transactions during the day. This type of anomaly is traditionally very difficult to detect, since it occurs within a range of a seasonally repeating metric. Traditional, threshold based detection will not detect these types of incidents. The price tag is self-explanatory: less business coming in, disgruntled customers, and eventually — customer churn and loss of brand reputation. But when using AI, automated anomaly detection employing machine learning can detect these issues up to 80% faster than other (manual) detection techniques, directly leading to less down time and improved customer experience.
Most major financial institutions are well aware of the imperative for action and have embarked on the necessary transformation from manual to automated processes. Those who can’t or won’t replatform risk becoming hopelessly uncompetitive against the financial services companies who can be faster, cheaper and more customer-focused by putting AI-based technology first.
David Drai is CEO and a co-founder of Anodot , where he is committed to helping data-driven companies illuminate business blind spots with AI analytics. Previously, he was CTO at Gett, an app-based transportation service used in hundreds of cities worldwide. He also co-founded Cotendo, a content delivery network and site acceleration services provider that was acquired by Akamai Technologies, where he served as CTO. He graduated from the Technion – Israel Institute of Technology with a B.Sc. in computer science.