Megaladata for Banks: The Advantages of a Low-Code Analytics Platform

This article explores how a low-code analytics platform like Megaladata can transform a bank's lending operations, boosting decision-making speed and drastically reducing the time required to implement changes. It focuses on the following topics:

• Adaptability

• Responsiveness

• Reliability

• Megaladata's Core Capabilities

• Megaladata as a Universal Low-Code Tool

• Seamless Integration with Credit Bureaus and External Data

Lending is a core activity for any bank, often accounting for over 50% of its operations. The profitability of a financial institution is therefore directly tied to the structure of this business process. Two key criteria determine the success of a modern lending operation:

• Adaptability (Internal Environment)

• Responsiveness (External Environment)

Adaptability refers to the speed at which changes can be made to a process—the ability to quickly redesign and reconfigure workflows. This metric is often called time to market (TTM). In today's market, an optimal TTM is considered to be one day.

Responsiveness refers to the speed of decision-making on a loan application—the ability to quickly provide a "yes" or "no" to a potential borrower. These metrics are known as time to yes and time to no. For modern lending, time to yes should not exceed a few minutes, while time to no should be under one minute.

Due to intense competition in the banking sector, responsiveness is increasingly critical. The bank that responds first to a borrower's application is often the one that secures their business.

Further on, we examine how the Megaladata low-code analytics platform helps financial organizations enhance their lending processes in terms of both adaptability and responsiveness.

Megaladata for Adaptability

Traditional software development is an expensive and time-consuming process. Debugging, testing, and deploying new software can take months, by which time the solution may already be outdated. This forces banks to expend significant financial, temporal, and human resources.

Megaladata streamlines this with a visual design environment built from pre-made components, allowing for the rapid creation of analytical scenarios. Thanks to its user-friendly, intuitive low-code architecture, the platform empowers business specialists to construct analysis workflows with minimal involvement from the IT department.

These features allow an organization to:

• Quickly design and, when necessary, reconfigure decision-making pipelines.

• Resolve challenges directly within business units, minimizing requests to other departments and simplifying internal approval procedures.

• Enable business analysts or team members to solve simple tasks without diverting the scarce resources of senior data scientists or IT specialists.

• Reduce TTM from several weeks to as little as one day.

Megaladata for Responsiveness

A typical borrower rarely waits for a response from every bank to which they have applied. They will usually sign a contract with the first institution that offers acceptable credit terms. This means the fastest financial institution attracts the highest quality credit portfolio. Why? Because the next, slower bank will only be able to offer its products to clients the first bank has already rejected.

Industry analysis shows that a significant portion of potential borrowers—often as many as 54 percent—accept the first reasonable offer they receive and do not consider alternative options. In this highly competitive environment, the speed of a decision is not just an advantage; it is a key criterion for success.

By implementing the Megaladata low-code analytics platform, fintech companies can:

• Quickly qualify and profile potential borrowers.

• Rapidly determine individual loan parameters: limit, term, rate, and other conditions.

• Minimize time to yes and time to no by automating the entire procedure.

• Increase the capacity of the credit pipeline several times over.

Megaladata for Reliability

Reliability is another critical factor for success, affecting both the internal and external operations of a fintech company. The lending system cannot afford technical interruptions or failures, as any downtime in the credit pipeline leads to significant financial losses and customer churn.

Borrowers who face delays do not simply wait; they move on to competitors. Every hour of downtime diminishes a bank's competitiveness while increasing the appeal of its rivals. This does not even account for the reputational damage caused by decreased customer loyalty. The uninterrupted operation of the decision-making system is a key requirement.

Reliability is achieved by automating the maximum number of processes—minimizing the human factor—and by building a stable infrastructure. Megaladata supports operation in a fail-safe cluster mode, which ensures the decision-making system functions without interruptions or failures.

Megaladata's Core Capabilities

Megaladata is a universal platform, and its benefits extend beyond the primary loan decisioning system. The platform offers additional capabilities that ensure more informed decision-making:

Evaluation of Default Probability: A well-developed scoring model can significantly increase the profitability of a loan portfolio through optimal risk management. Megaladata allows you to build models, evaluate their quality, compare them, and track their performance over time to promptly adjust them to the bank's needs.

Collection Probability Analysis: This is essential for ranking customers and assessing the value of a non-performing loan portfolio. This information is used to select the best strategy for working with debtors and recovering the maximum amount of debt.

Pre-Litigation Repayment Assessment: This helps reduce legal costs associated with claims and collections. Using a predictive model, it is possible to identify categories of clients who are likely to repay their debt before legal proceedings begin.

Risk Rating Analysis System: Designed to help institutions meet stringent regulatory requirements, such as IFRS 9 and Basel III/IV. By implementing a robust internal credit risk rating system, banks can potentially optimize their capital reserve requirements and ensure compliance.

Megaladata can be used to implement custom logic from scratch. We also offer ready-made application solutions built on the platform:

• Megaladata Decision Maker: Automates complex decision-making in lending, microfinance, and insurance. It can be used to build a credit pipeline for individuals and businesses, form a consolidated credit history, and underwrite clients.
• Megaladata Process Mining: Enables deep analysis of lending processes based on information system logs. The solution helps optimize business processes to increase the speed and responsiveness of the decision-making system.
• EcoAsset.AI: A powerful tool for banks' treasury and asset management teams, developed in collaboration with ADEAL Systems GmbH. It enhances data quality and transparency across loan and asset portfolios, enabling ESG compliance, personalised green lending, and risk-optimized pool management and monitoring.

A Universal Low-Code Tool

To summarize, Megaladata provides key advantages as a low-code product:

Adaptability: Fast development of data analysis workflows using a low-code approach and ready-made components. This empowers users and frees up IT resources, dramatically reducing time to market.

Responsiveness: Radical reduction in time to yes and time to no through the automation of the credit pipeline and rapid client profiling with integrated scoring systems.

Reliability: Guarantees uninterrupted operation of the credit pipeline through process automation and a fault-tolerant infrastructure. Using a cluster of Megaladata servers ensures high availability and allows for easy scaling as application volume grows.

Seamless Integration with Credit Bureaus and External Data

A lending decision is only as fast as the data it relies on. A critical step in any modern credit pipeline is the rapid retrieval of a borrower's credit history from bureaus like Experian, Equifax, or TransUnion.

Megaladata is designed for this reality. The platform's robust integration capabilities allow for the creation of seamless, automated connections to any third-party data source via APIs. This means a bank can:

• Instantly query credit bureaus as soon as an application is received.

• Automatically parse complex credit reports to extract key fields like credit score, payment history, and existing debt.

• Combine bureau data with internal data and other sources (e.g., fraud prevention services, bank transaction data) within a single, unified workflow.

By automating this crucial data-gathering step, Megaladata directly attacks a primary bottleneck in the lending process, turning a potential delay into a near-instantaneous action. This is fundamental to reducing time to yes from minutes to seconds and building a truly responsive lending operation.

Don't let legacy systems dictate your speed. Contact us today to learn how you can automate your lending processes, reduce your time to yes, and secure the best borrowers.

Read more about Megaladata-based solutions here:

• EcoAsset.AI
• Solutions for Finance on Demand
• Megaladata-Based Applications From Our Partners