How real-time data technology drives AI in financial services
Roshan Kumar, Senior Product Manager, Redis Labs, discusses the capabilities of Artificial Intelligence-driven solutions which are having an impact on the financial sector, helping it to propel innovation.
About 50 years ago, Barclays opened its first automatic teller machine (ATM) and changed the way we did banking. To this day, the ATM has been one of the most disruptive innovations of the financial sector, which forever seeks to stay on the leading edge of innovation. These days, you can deposit a cheque just by taking a picture of it using your mobile phone. Behind the scenes, Artificial Intelligence (AI) deciphers the numbers on your cheque and deposits the appropriate amount into your account.
Of course, AI does a lot more than that. Financial services firms use AI-driven solutions for fraud detection, risk assessment, asset management, investment portfolio optimisation, stock predictions based on social trends, customer engagement and much more.
Emerging challenges for AI in financial services
As financial services organisations adopt the latest and greatest AI-based solutions to support their evolving business needs, they encounter new problems they didn’t face before, including demand for:
1. Instant response at scale: In today’s on-demand, always-on mobile era, people expect instant responses from every app or website they use. A delayed response may result in losing a customer, missing a stock trade or failing to flag fraud. To meet this expectation, AI solutions must run thousands, if not millions, of decisions in a split second and do so cost-effectively at scale
2. Better quality: What distinguishes AI from other computational solutions is its ability to learn and adapt to new situations. This process of learning must result in more accurate and applicable responses as time passes. However, quality improves only if the AI solution is able to capture and process all of the data points available and recalibrate its decision models effectively
3. Autonomous AI solutions: Network connectivity is usually taken for granted, leading to centralised solution designs. However, the ATMs and AI solutions of the future must work even when they are disconnected from each other. Financial services firms are working hard to push their solutions to the network edge, where autonomous AI solutions can learn and make decisions on their own
How financial services can drive their real-time AI needs using high-speed databases
Today’s most effective database platforms are constantly evolving to deliver instant responses for the applications they support. In-memory databases in particular are enabling hackers, innovators, developers and architects to deliver solutions for the instant economy.
