Nowadays artificial intelligence (AI) has become the key driver for automation. It has transformed from a niche tool into a cost-effective solution for widespread business adoption. It enables minimization of routine tasks and human error, the optimization of complex decision-making processes, and the creation of entirely new, more efficient business models.
Therefore, AI opens new opportunities for all businesses, signifying not just another IT system upgrade, but a complete overhaul of operational processes and customer interactions. It is especially essential for banks where financial organisations not only have to stay digital but also rapidly adapt according to the clients’ needs.
RunRule Solutions, as an experienced team with its own low-code application platform, has deeply integrated neural networks into the 2RS Platform.
- Applying ML models into business applications – including systems for automated scoring, credit risk assessment, collections, sales, and marketing.
- Manage every AI tool from one central platform. This lets you use your custom-built AI and connect to any outside system through a simple API.
- Embedding unique machine learning-powered widgets directly into user interfaces.
- Leveraging LLMs within the 2RS Platform – providing AI-powered tools for application development and debugging.
AI in Low-Code Platform – Saving 7.5K Team Hours
AI integration into 2RS is an important part not only of the system’s strategic evolution, but also the increase in business’ economic efficiency. Today the 2RS Platform raises the productivity of junior developers by 30%, saving senior developers’ time otherwise spent on onboarding and reviewing standard tasks. Due to machine learning and neural networks, a team lead spends 1.5-2 fewer hours per day on each junior, which for a large company translates into roughly 7,500 hours saved.
Enhanced with AI-powered tools the 2RS boosts low-code development productivity. The service for interacting with LLMs, preconfigured for the banking industry, is adapted to the platform’s internal programming languages and integrates seamlessly.
- Smart documentation search
The upgraded search engine is based on vector databases. It allows fast answers to be drawn from deep contextual analysis rather than simple keyword matching.
As a result, instead of manually browsing through multiple sections of documentation, this feature delivers accurate answers quickly, provides a concise summary and links to the most relevant articles. Therefore, new team members onboard faster, while team leads are freed from repetitive consultations.
- Automated development and testing
The built-in AI assistant supports onboarded developers throughout the entire workflow by:
- generating platform objects and producing basic application frameworks
- helping to write, extend, and optimize code prepared by analysts and system designers
- automatically creating unit tests to catch errors early and suggesting improvements for functions, scripts, and processes
- explaining how the platform and its business applications work.
What is more, juniors will be able to communicate with the AI-assistant in a conversational interface, rating the neural network’s responses and helping it to continuously improve.
- Voice input
The platform now accepts spoken input, so users can dictate descriptions or fill out forms without typing. The system recognizes around 100 languages and automatically converts speech into text, making the process more convenient for both developers and end users.
AI Banking Automation: Collection, Credit Scoring, and Customer Acquisition
Owning cutting-edge AI technology is an important step for businesses to become efficient. However, it does not guarantee success unless the company is able to weave neural networks seamlessly into its business strategy and day-to-day operations.
As top research firms like Gartner regularly point out, the real challenge is not acquiring the most advanced algorithm, but embedding it with the purpose to solve concrete business problems. AI creates value when it stops being a standalone IT project and becomes part of core processes — from credit risk scoring and fraud detection to tailoring customer experiences.
RunRule’s team has extensive experience integrating AI agents into the financial industry. Here are some approaches to how neural networks are applied in systems for financial organisations.
- Collection
With Debt Collection, banks launch machine learning models like logistic regression, gradient boosting, or neural networks. A common case is portfolio segmentation, which speeds up debt recovery. These models can:
- predict the chance of repayment without contacting the client
- estimate the risk of default at different stages (e.g., soft collection)
- choose the best operator profile for each case.
For example, in one top bank in the country operators don’t call customers if the model predicts — with 81% accuracy — that they will repay within three days on their own.
Debt Collection also supports external ML widgets to create the “workplace of the future.” Operators can see real-time insights such as the customer’s mood (based on voice analysis) and receive tips for how to talk to them. The system also uses AI agents to assign tasks to call robots and check call results afterwards.
- Credit scoring and risk assessment
Risk Decision Engine for Lending lets banks run ML models in real time to check creditworthiness and suggest the best offers. Results can be used by risk managers to adjust application processing and segmentation. The system automatically sets approval and rejection rules.
It is a useful tool which helps banks build ML models with their own data and connect them to decision-making. For instance, one of RunRule’s clients created a default risk model that approves products automatically if the risk is very low and no blocking factors are found.
- Personalized marketing
Campaign Manager, which supports both real-time campaigns and scheduled activities, enables banks to deliver tailored customer journeys and generate the NBO (Next Best Offer). Based on CRM, loan origination data, and transaction history, it allows banks to:
- forecast customer churn probability
- estimate the likelihood of product purchase
- choose the best communication channel and timing.
The Next Best Offer model gives the possibility to determine which customer acquisition scenario will maximize profitability in a given timeframe. Thus, one of our clients increased conversion rates by 40% — lifting post-contact sales from 1% to 1.4% across the entire product line.
Multi-Agent System for Better AI Answers
Today 2RS Platform works as a single gateway that connects and manages different AI agents, large language models, and machine learning models. RunRule Solutions is focusing on simplifying development with AI, thus, neural networks will be able to produce reliable answers handling large and complicated tasks.
For this reason, RunRule’s team is building tools — a multi-agent system, where the neural network generates code in several contexts at once. As a result, it will give AI a clear framework and raise the performance of smaller neural networks to the level of LLMs.
“The future of AI in enterprise solutions lies not in isolated models, but in orchestrated ecosystems where multiple agents, models, and technologies complement each other. At RunRule Solutions, our work on a multi-agent system within the 2RS Platform is aimed at exactly that — providing structure, governance, and scalability to AI development. This approach ensures that neural networks are not just powerful, but also reliable, secure, and business-driven. For banks and enterprises, this means that AI can finally evolve from being a promising experiment to becoming a dependable foundation for mission-critical decision-making and customer engagement,” said the CEO of RunRule Solutions Aman Mamyrbekov.
RunRule Solutions is committed to accomplishing the business transformation and making it more efficient. The 2RS Platform combines low-code technology and integrated AI which empowers organisations to rapidly build and deploy custom automation solutions with minimal IT dependency.