Why Heavily Regulated Markets are Turning to Real-Time Machine Learning

Imagine you’re furnishing your dream kitchen. The furniture is already ordered, and all that’s left to buy are the appliances. Your online shopping cart is full, but the final amount tempts you to apply for a loan. You fill out the application form, provide your details and wait for a decision. The loading indicator spins for several minutes and frustration grows – the purchase begins to seem unlikely.

Now, transfer this situation to the world of a data modeling specialist. You know credit risk assessment mechanisms inside and out: you build customer profiles, assign weights to key metrics and train models that quickly deliver accurate decisions. Your model is running smoothly and ready to truly improve customer service processes. However, the implementation challenge remains – integration with existing systems requires the collaboration of other teams, and wait times are lengthy.

What if there was a platform that allowed models to be run and integrated into systems without the need for additional code and lengthy implementation processes?

In a recent initiative, Software Mind experts developed a solution that enables the launch and integration of ML models using only configuration. Along with eliminating the need for additional code, shortening implementation cycles and ensuring rapid, reliable responses for end users, this platform successfully supports projects in heavily regulated environments, like the financial sector.

Driving strategy through a real-time ML platform

For such a platform to effectively achieve business goals, it must meet rigorous standards in key areas:

Scalability and fault tolerance: Processes must handle significant traffic and dynamically adapt to changing workloads. Processing pipelines operate 24/7, with no downtime allowed outside of scheduled maintenance windows.

A/B testing capability: The platform allows for parallel execution of multiple models or different versions of the same model, with full control over the traffic distribution between them. This enables teams to safely test and gradually implement new solutions.

Auditable and traceable: Full operational transparency is essential. Audit information supports customer complaint processes and creates a closed feedback loop for continuous model evaluation and improvement.

Real-time monitoring: Continuous monitoring is the foundation for high availability and proper operation. Measuring specific KPIs provides operational certainty, especially in mission-critical applications based on ML.

Secure data protection, access authorization and compliance with industry regulations are an absolute must, implemented at every level of the platform.

Solution architecture

This real-time ML platform is designed for maximum performance, scalability and low latency in real-time data processing. Its architecture is based on three main pillars that work together to ensure the reliable deployment of ML models in production environments.

Queue system

The foundation of asynchronous communication is a modern queue system (Apache Kafka). This allows the platform to efficiently manage data flows, minimizing latency even under heavy loads.

• Asynchronous Communication
• Minimized Latency
• Supports high traffic with a backpressure mechanism
• Easy Integration with Other System Components

Stream Processing Execution Engine

The heart of real-time processing is the powerful Apache Spark engine. It enables lightning-fast execution of operations on data streams, with full scalability and support for modern programming languages.

• High processing efficiency
• Support for real-time stream processing
• Excellent scalability
• Coding in modern and popular languages (e.g., Python)

NoSQL Database as a Feature Store

The third pillar is a fast NoSQL database (Apache HBase), acting as a Feature Store. It allows for rapid data enrichment with additional features without impacting system availability.

• Very low latency when enriching data with additional features
• Efficient search across large datasets
• Ability to update and refresh the Feature Store without downtime

Thanks to the synergy of these three pillars, this real-time ML platform guarantees not only rapid model integration but also their stable and secure operation. They can be run both in on-premises environments (e.g., within Hadoop clusters) and within managed cloud services (e.g., Amazon Kinesis, Amazon EMR, and Amazon Elasticache).

Real-time ML use cases

Software Mind’s real-time ML platform has been successfully implemented in numerous projects in the financial sector, where fast decisions, regulatory compliance and effective risk mitigation are crucial.

Automating credit decisions in ecommerce and physical retail channels.

In this project, the platform enabled full automation of credit decisions in both ecommerce channels and physical points of sale. The goal was to provide lightning-fast, fully automated application assessments using a ML model that predicted the probability of default. A customer shopping online could receive a credit decision in less than three seconds – as an optional step in the transaction financing process. Similarly, in physical stores, consultants using a dedicated sales application received a response in under five seconds, even for applications submitted by groups of customers. This significantly reduced the credit default rate, provided high customer satisfaction thanks to immediate responses and the entire complaints process was fully covered by the platform’s auditability mechanisms.

Automatic type of client classification

The project focused on online, automated assessment of customer parameters, such as segment, sector and company size, supporting scenarios for retail and corporate banking. In the onboarding process for new customers, the assessment was based primarily on self-declared data and public information, while for existing customers, the system supported various branches – from leasing to brokerage offices. Additionally, periodic reporting was introduced, which detected changes in the TOC and generated alerts requiring the bank’s intervention. The result was full compliance with regulatory requirements, precise real-time determination of customer type based on an enriched dataset, scalable and cost-effective periodic calculations, and high algorithm reliability thanks to artifact versioning and full traceability.

Rapidly calculating customer risk levels

In this implementation, the platform utilized a comprehensive algorithm utilizing a wide range of data sources, from “high risk” flags, through transactional and geographic data, to relational and product data. Overall risk was presented as a weighted average and easily classified as green, yellow, or red. The solution supported both retail banking (accounts, mortgages, unsecured loans, and co-signing) and corporate banking (loans and deposits). This ensured the bank fully complied with regulatory requirements, significantly improved resilience to high-risk customers and robust business decision support with reliable, measurable risk metrics.

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FAQ

How can a real-time ML platform enable rapid deployment of ML models?

By enabling models to be launched and integrated into existing systems through configuration alone. This eliminates the need for additional coding and significantly shortens implementation cycles.

How does the platform ensure auditability and regulatory compliance?

It delivers full operational transparency through comprehensive audit logs and traceability. These mechanisms support customer complaint resolution and enable closed feedback loops for ongoing model evaluation and improvement.

What deployment environments does the platform support?

The platform can be deployed on-premises, including within Hadoop clusters. It also fully supports managed cloud services, such as those provided by AWS.

What are the primary financial sector use cases for the platform?

The platform powers real-time automation of credit decisions, client type classification, and customer risk level calculation. These solutions provide responses in seconds, ensure full regulatory compliance, and substantially reduce default rates.