Data and Artificial Intelligence in Banking

Adoption of Artificial Intelligence in the Banking Industry

The banking industry is one of the leading industries in terms of Artificial Intelligence adoption and according to IDC by 2022 over $5B will be spent on AI applications, including automated threat intelligence & prevention systems and fraud analysis & investigation systems. This is mainly due to AI’s ability to improve financial firms administrative and customer service levels, provide predictive analytics functionality, improve fraud detection, automate and accelerate core functions while saving money all at the same time. The Financial Brand states that adopting AI powered solutions is expected to save over $1T by the end of the decade in the banking industry worldwide.

With overall digitalization of the services and the increase of measurable data that can be collected and analyzed for each customer, data insights are as important as ever, in order to achieve efficient customer communication and reduce churn rates. With constant growth in the cost of new client acquisition and improved methods in increasing client retention, adopting an AI-powered solution makes more sense than ever before.

Data Gathering and Management

Due to many regulatory constraints, banks are already collecting and gathering immense amounts of data, and banks are the ones who have the most to gain from strong data strategy implementation and leveraging of AI. Implementing good robust data gathering and management techniques will not only allow compliance with all the regulatory requirements, but also will allow monetizing the data and bringing additional value to banks. Regulators impose strong limitations on applicable ML and AI solutions due to transparency and explainability constraints. Consumer vulnerability should be identified and addressed very carefully as it has become increasingly important due to the current economic situation. While all this might seem a big limitation, it actually creates a good opportunity to educate the personnel working with AI and data in general, and to make sure that all the calculations are absolutely bulletproof.

The biggest challenge however, is the existing infrastructure and technology stack, and it’s inability to easily incorporate modern decisions into existing pipelines. Bespoke solutions are often required on a case by case basis and custom approaches are required in order to incorporate ML and AI solutions into existing infrastructures. Cost-savings becomes the main priority here, not only when talking about the impact to the final solution, but also when we talk about the development, implementation, and incorporation of costs. The selected toolset should be very flexible and capable of coping with upcoming challenges, including the economic situation, transparency, and potential regulatory requirements.

More than half of AI related projects across the industry never make it into production. This is mainly due to the fact that AI related systems are way more dependent on the incoming data than most other computer systems. The volume and quality of data directly affects the quality and performance of AI algorithms, and what’s more important is that there is virtually no way to know how much predictive power there is in the data without building a proof of concept system or doing a feasibility study. Most projects usually stop here, not because of the usefulness of the AI solutions themselves, but due to the cost associated with implementing the system into production. Scaling, calculation time, and integration challenges are often overseen when starting an AI project, and later become the main showstopper for successful implementation. MLOps is one of the hottest topics nowadays and should be considered before any project starts.

The Era of Data Science Platforms

Usually hiring of external data scientists or consultants is required to properly build a production ready solution. And the reality is that there is a very limited amount of professionals adept in both banking and AI at the same time. One of the workarounds for this challenge is giving existing SMEs and analysts the opportunities and toolsets to perform AI related tasks. This is actually more beneficial than hiring expensive third-party personnel. It’s an era of end-to-end data science platforms. The main needs are the increase in capacity of the analytics and data science teams within budget constraints. Also it is important to maintain existing headcount, explainability and transparency, and collaborative development with a high level of automation and customization. The current proposition on the market is not cost-efficient and requires significant investment and adoption time.

With these prerequisites in mind, our partner Datrics.ai started building their drag-n-drop AI platform – the fastest way to create and operate machine learning solutions. The platform allows end-to-end developments of data and AI projects, easy collaboration between different roles (SMEs, analysts, developers), simple hosting and integration. The platform requires zero coding skills and all the development is done through an intuitive and user friendly interface. The platform automatically scales depending on the load and the amount of data. Also, Incorporation is made easy through the use of REST APIs for the finished pipelines.

If you would like to learn how to make your business competitive and adaptable in today’s financial world, join our free webinar Digital Transformation for Banks: Trends and Challenges. This is the third webinar held in the series of webinars by Sigma Software aiming to inspire businesses to move forward, including expert views on how to get situations under control and stay successful.

• #AI/ML
• #BI and Big Data
• #Finance and Banking