Why do FinTechs and financial institutions need MLOps, ML, and AI solutions in times of dynamic interest rates and tightening regulations?


In recent years, the financial industry has undergone significant transformations marked by a series of new regulations and frequent fluctuations in interest rates. To navigate this ever-evolving landscape successfully, FinTechs and financial institutions must find agile ways to adapt their strategies. Read on to explore effective strategies for implementing organizational changes and understand how MLOps, ML, and AI solutions build a competitive edge in today’s dynamic financial universe.


Financial market volatility

The constant changes in the financial market in recent years pose a real challenge for FinTechs and financial institutions.

Events such as changes in the NBP interest rates, new regulations reducing non-interest costs of credit, making credit scoring assesments mandatory, or the supervision of loan companies by the KNF have triggered a lot of uncertainty in the industry. At the same time, increasingly restrictive regulations on the protection of personal data are being introduced, further complicating the effective risk assessment of customer data.

All these changes require the industry to continuously adjust its offerings and strategies. The flexibility of FinTechs in adapting to these changes has become a key element in maintaining market position.


The constant need for adaptation

Each change in market conditions or business assumptions serves as a catalyst for changes within organizations, processes, and systems. The key lies not only in the ability to analyze and draw conclusions but also in the speed of implementing new  solutions. The adaptation process consists of several phases:

1. Data analysis and inference

A crucial element of the adaptation process is conducting a deep analysis of the situation to correctly understand the new market context. Organizations that can gather data and leverage advanced tools for analysis, including artificial intelligence, gain an advantage in deriving more accurate and often non-obvious conclusions.

2. Designing changes

The formed conclusions become the foundation of a new strategy determined by the management team. This strategy should holistically consider all necessary changes in the organization’s systems, processes, and structures.

3. Testing and calibration

New solutions must undergo rigorous testing to verify their results and adapt them to real market conditions. This is the stage where the first challenges related to adaptation often arise. Organizations must be prepared for potential calibration of their plans.

4. Implementation of changes

Effective implementation of changes on a large scale is the final and crucial stage that requires collaboration between IT, operational, and management teams. Here, additional challenges emerge in coordinating actions and minimizing turbulence to the organization’s functioning. Successful implementation provides the company with momentum for the next few months of operation—usually until another significant change in market conditions emerges.

Efficient execution of this process is a significant challenge for most companies, incl. FinTechs. In the face of a dynamically evolving financial environment, FinTechs strive to adapt their risk management methods.


Traditional risk management

The conventional approach to risk management relies on manual, time-consuming assessment processes, where manual analysis and data collection are crucial elements of the process. Unfortunately, models created in this way are typically simplistic, leading to low-quality predictions, especially when dealing with new clients.

As a result of this approach, financial offerings become inflexible and poorly tailored to the individual risk profile of the customer. An inefficient assessment model systematically restricts access to services for good customers who could benefit from our offerings but cannot due to incorrect assessment. Manual processes also make it difficult to make rapid changes in response to emerging changes in the environment.

Therefore, the traditional approach to risk management hinders the growth of FinTech companies and prevents the realization of the full market potential.


Evolution of risk assessment systems

The answer to inefficient financial offerings lies in adopting modern risk management strategies. These ensure a personalized approach to each customer while still maintaining speed and high prediction accuracy.

Traditionally, institutions have relied on straightforward rules embedded in Customer Relationship Management (CRM) systems and scoring cards. These rules were typically based on data from credit bureaus and credit histories, providing only limited precision in risk forecasting.

Modern organizations are transitioning to systems based on Machine Learning (ML) and Artificial Intelligence (AI). ML models can analyze vast amounts of data, even capturing subtle patterns within specific customer cohorts that were previously undetected. These systems are self-learning, meaning their ability to assess risk continually improves over time.


Companies’ needs for risk assessment

When examining the risk assessment systems used by companies, it’s essential to consider several key needs that this systems should address in today’s dynamic world:

  • Short time to market for introduced changes in data sources, models, or rules
  • High flexibility in managing the current risk appetite
  • The ability to conduct A/B tests (champion vs. challenger)
  • Continuous monitoring of the process and models accuracy 

In response to these needs, companies should seek solutions with the following characteristics:

  • Consolidation of process elements in one place for efficient management
  • Transparent process definition through low-code mechanisms and a drag-and-drop interface
  • Flexibility to adjust the system to evolving needs
  • Flexibility in the structure of algorithms and models, as well as integration with any data sources (both internal and external)
  • Automatic export of analytical and audit data for process monitoring


Integrated vs. distributed architecture

Modern risk assessment systems in FinTech often rely on microservices architecture or multiple individual services, theoretically providing flexibility and scalability—two critical features for risk assessment systems. However, in practice, this decentralization introduces new challenges.

The dispersion of various system components can lead to integration problems, maintenance issues, and complex data management. Every change, even the smallest, requires the standard process of analysis, implementation and deployment to be run. This model results in substantial IT resource consumption and complicates communication between system elements, making A/B testing also challenging. As a result, the typical implementation time for a change extends to months and even years, potentially resulting in the loss of market position to more agile competitors.

In this reality, the solution for FinTechs becomes an integrated architecture that combines all key elements of the risk assessment process and necessary data in a unified environment. Systems based on this approach require only basic IT skills, enabling small teams or individuals to make and implement changes without involving multiple departments in the company. Additionally, a transparent graphical representation of the process facilitates understanding the entire system, and all data is securely processed and generated within a single application. As a result, the implementation time for even extensive changes shortens from months and years to days and weeks.


Harness the potential of integrated infrastructure in risk assessment

Algolytics’ Scoring.One platform, a European finalist in the Artificial Intelligence (AI) in Finance Global Challenge, serves as a response to the evolving needs of the FinTech industry in the face of dynamic challenges in the financial market. With its integrated, flexible, and transparent infrastructure, Scoring.One provides companies with a comprehensive solution for effective risk assessment and fraud detection. If you’re looking to harness the full potential of AI and ML, along with the capabilities of MLOps in your organization, reach out to us at https://algolytics.com/contact/ and discover how an innovative approach to risk assessment can strengthen your business.

Gabriela Kocurek


Specjalizuje się w prawie nowych technologii i regulacji rynków finansowych, prawie własności intelektualnej, prawie ochrony danych osobowych oraz prawie zamówień publicznych. 

Jest ekspertem w obszarze regulacji dotyczących usług chmurowych oraz outsourcingu usług IT, z uwzględnieniem specyfiki sektora finansowego. Wspiera klientów w obszarze zamówień publicznych, z uwzględnieniem specyfiki zamówień w sektorze IT.


Doradza w szczególności klientom z branży FinTech, IT, cyberbezpieczeństwa, e-commerce i branży nowych technologii:

  • Posiada bogate doświadczenie w przygotowywaniu i negocjowaniu umów IT, umów wdrożeniowych oraz umów na świadczenie usług IT w modelu SaaS a także umów licencyjnych, dotyczących przeniesienia know-how, transferu praw własności intelektualnej jak również umów dotyczących komercjalizacji wyników prac badawczo – rozwojowych.
  • Wspiera klientów z sektora FinTech w dostosowaniu umów i wdrażaniu wymogów regulacyjnych właściwych dla sektora finansowego. Doradza i wspiera klientów w negocjowaniu umów IT w reżimie outsourcingu bankowego, inwestycyjnego, chmury obliczeniowej i outsourcingu w rozumieniu wytycznych EBA.
  • Doradza w zakresie umów IT oraz ochrony danych osobowych podmiotom z branży IT Security.
  • Współuczestniczyła w audycie procedur ochrony danych osobowych w grupie spółek o zasięgu globalnym.
  • Doradza klientom w zakresie prowadzenia kampanii marketingowych o zasięgu międzynarodowym.
  • Wspiera klientów kancelarii w postępowaniach o udzielenie zamówień publicznych. Doradzała klientowi kancelarii w postępowaniu o udzielenie zamówienia publicznego na wdrożenie Platformy Kanałów Elektronicznych przez Bank Gospodarstwa Krajowego oraz z sukcesem reprezentowała klienta w postępowaniu dotyczącym tego zamówienia przed Krajową Izbą Odwoławczą.

Kwalifikacje i uprawnienia zawodowe

Radca prawny przy Okręgowej Izbie Radców Prawnych w Krakowie.

Absolwentka studiów podyplomowych na kierunku Prawo Zamówień Publicznych na Wydziale Prawa i Administracji Uniwersytetu Warszawskiego.

Absolwentka studiów magisterskich na kierunku Prawo na Wydziale Prawa i Administracji Uniwersytetu Jagiellońskiego.

Absolwentka studiów licencjackich i magisterskich na kierunku Administracja na Wydziale Prawa i Administracji Uniwersytetu Jagiellońskiego.