• Roshan Kumar, Senior Product Manager at Redis Labs


• 13.02.2019 07:15 am
undisclosed

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. However, this is just on the surface because 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 so on. 

Three 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. Machine learning for 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 can capture and process all 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 in-memory databases support new AI use cases in financial services 

Today’s most popular database platforms understand the need to deliver instant responses for the applications they support. Open source projects are mobilising hackers, innovators, developers and architects to deliver products for the instant economy. 

Developers at financial services firms use these flexible platforms to deliver real-time personalised user experiences, job and queue management, high-speed transactions, user activity tracking, product recommendations and more. For instance, companies have embraced open source projects, like Redis, to power specific AI needs such as:

1. Instant response for decision models:AI solutions react to situations they encounter by collecting data points, processing them and running them through decision models. For example, an option trading company uses Redis to compute the optimal price for buying and selling options in real-time. Redis-ML, the Redis module for machine learning, enables AI to run pre-trained neural network or tree ensemble models at unprecedented speed. This module is widely used by the financial services industry for fraud detection. 
2. Faster model training:Most data scientists spend a lot of effort calibrating and re-calibrating their AI models. But since AI models only understand numbers, all text data must be mapped to numerical data during this process. The faster you convert it, the faster you can train your AI solution. By using Redis as a dictionary, data scientists are performing over a million conversions in less than a second. One financial services company that applies AI to compliance and governance uses Redis as a data dictionary, so it’s able to retrain models and deliver better results many times faster.
3. More real-time data:The financial sector collects more real-time data than most other industries, including information about stock trades, commodity prices, interest rates and currency transactions. A stream database can help manage these volumes by ensuring all the data points are fully captured and processed. With the Redis Streams data structure, Redis connects a multitude of producers and consumers, and since the database is binary safe, it can also capture streaming audio, video and pictures. This is critical for a portfolio management company that uses Redis to track user activity around research interests in order to make personalised investment recommendations. 

Given the speed at which innovation is occurring in the financial sector, it won’t be too long before we see an autonomous vehicle that also happens to be an ATM (or the bank itself), stops at your door, recognises your face, lends you the money you want, and recommends how to rebalance your investment portfolio. When that happens, in-memory databases will be a key component powering the AI engine.