As we step into 2024, the banking and finance sector may be poised for a revolutionary year marked by substantial changes and innovations. These trends are influenced by the evolving customer expectations from the banks, the technological landscape, regulatory requirements, and the need for greater efficiency and risk management. Business Rule Engines (BREs) are not untouched by these trends and shape the way banks and financial institutions look at BREs. Here are some of the prominent trends we are hearing from our customers while we move into 2024.

1. Real-time Processing and Decision-making:

BREs are evolving to handle real-time data processing, which is crucial for applications like personalized offers, fraud detection, dynamic pricing, and next best product recommendation.

Instant offers have become an important differentiation for banks and financial services providers in the fast-paced world of banking, where client expectations are changing quickly. Advances in artificial intelligence (AI) and the incorporation of business rule engines into banking operations have sparked a trend towards 'right here, right now' offers. The ability to process transactions and make decisions in real time is becoming increasingly important.

Financial institutions are putting customer value at the center of personalization efforts by enabling personalized recommendations based on existing data, including customer profiles, touchpoints, customer propensity, and affinity to identify the target segment. While the outputs are individualized, the inputs and rules that produce them are written with no code, are unified, and are centralized to proactively present the most relevant financial product to the customer based on their needs.

2. Enhanced User Experience and Interface:

There is an emphasis on improving the user experience and interface of BREs. This involves making BREs more accessible to non-technical users, with intuitive design and easy-to-use no-code rule authoring tools. Simplifying the interface and allowing business users to write rules can help in quicker rule deployment and modification, thus reducing the dependency on IT staff.

3. Increased Integration with AI and Machine Learning:

BREs are increasingly being integrated with artificial intelligence (AI) and machine learning (ML) technologies. This integration enhances the capability of BREs to handle more complex decision-making processes, improve fraud detection, and offer personalized customer services.

AI and ML can analyze large data sets to identify patterns and trends, which can then be used to refine the rules within BREs for more accurate and efficient processing. Machine learning based rule-engine models make policies/rules adherence more efficient. The self-learning systems have helped them perform complex tasks such as fraud prevention, detection of money laundering, and market abuse much more quickly and efficiently. Using AI-driven name & face matching and face liveliness, the rule engine could derive a scorecard for indication of fraud.

4. Easy Regulatory Compliance and Risk Management:

The financial sector face deeper scrutiny from authorities and must adhere to an increasing number of laws and regulations and with every passing year the number of regulations that a financial institution has to deal with is increasing.  

As per a survey by MetricStream, over 19% of respondents suggested that it took them over a year to implement regulatory changes.

In addition, the complexity of dealing with huge quantities of data and an acute shortage of compliance experts makes it even more difficult to deal with the situation.

BREs are critical in ensuring compliance with various regulations. As new regulations emerge, BREs need to be adaptable and efficient in implementing these changes. BREs are also being used to manage risks more effectively, especially in areas like credit scoring, anti-money laundering (AML), and fraud detection.

5. Cloud-based Solutions and 'as a Service' Models:

There's a growing trend towards reducing the one-time spend and steering away from non-focus areas. Financial institutions are tilting towards cloud-based BREs and Business Rule-as-a-service models rather than on-premise models.  This shift allows banks to reduce operational costs, enhance scalability, and improve agility. Cloud-based solutions offer the flexibility to adapt to changing business needs and regulatory environments quickly.

6. Use of Alternate Data Models:

The traditional way of lending by banks involves the verification of different identity and financial documents like bureau scores, bank statements, collaterals, etc. that the borrower provides to help the financial institution assess the risk and creditworthiness of the borrower.

According to a recent study by global TransUnion titled 'Empowering Credit Inclusion: A Deeper Perspective on Credit Underserved and Unserved Consumers', over 160 million consumers were considered credit underserved in India and about 50 percent of India's population is credit unserved at the end of 2021.

Providing credit to such a large population is a priority for lenders and governments alike in 2024. Lending using alternate data like their social media profiles, their history with utilities, telecom bills, etc., and analyzing them in a structured manner can offer deep insights into the creditworthiness of the borrower.

With such a large volume of data at hand, the traditional methods will not suffice for assessing creditworthiness.

These trends indicate a dynamic and evolving role for business rule engines in the banking and financial services industry in 2024, driven by the need for enhanced efficiency, compliance, and customer-centric services. As technology continues to advance, BREs are expected to become even more integral to the functioning of the financial sector and the financial institutions need them more than ever in 2024.