• The Remarkable Impact of Decisioning Engines on Risk Management

    The financial technology (Fintech) industry thrives on effective risk management, given its reliance on cutting-edge technologies and data analytics. Within this context, decision engines have emerged as a true game-changer. These powerful tools enable Fintech and financial institutions (FIs) to capitalise on the potential of data-driven decision-making, automate operations, and strengthen their risk management capabilities. Let us take a closer look at the critical role that decision engines play in shaping risk management strategies in the ever-changing Fintech ecosystem.

    Understanding the Need for Effective Risk Management

    Effective risk management is critical in the Fintech industry, where businesses face a variety of risks associated with lending, investments, fraud, and regulatory compliance. Dealing with the intricacies of these risks necessitates advanced tools and strategies for accurately evaluating, tracking, and mitigating them. Traditional risk management methods, which rely on manual procedures and subjective assessments, are often inefficient and error-prone. Furthermore, they struggle to keep up with the massive amounts of data generated by the Fintech ecosystem. Enter decision engines, the solution to these problems.

    What Are Decision Engines?

    Decision engines are advanced software systems that use artificial intelligence (AI), machine learning (ML), and big data analytics to make real-time decisions and automate complex processes. These engines seamlessly integrate disparate data sources, employ advanced algorithms, and produce actionable insights that serve as the foundation for risk management strategies. By merging data analytics with business rules and predefined models, decision engines empower organisations to swiftly and precisely make informed decisions.

    How Decision Engines Improve Risk Management in Fintech

    Streamlining Data Analysis and Fraud Detection

    The financial impact of fraud on U.S. financial services and lending firms has escalated from approximately 6.7% to 9.9% when compared to the period before the pandemic. This implies that for every $1 lost to fraud, U.S. financial service companies now experience a cost of $4.00, while the figures stood at $3.25 in 2019 and $3.64 in 2020. 

    With decision engines, potential fraud can be detected early by spotting anomalies in “normal” behaviour or patterns. Decision engines excel at processing large amounts of structured and unstructured data, such as customer information, transaction records, credit scores, and market data, in a timely and efficient manner. Decision engines uncover significant patterns, trends, and potential risks that would otherwise go unnoticed by quickly analysing this massive dataset. This enables firms to make data-driven decisions based on complete and up-to-date information, reducing the likelihood of errors and improving the effectiveness of their risk management strategies.

    Enhancing Accuracy and Speed

    Traditional risk management practices are often manual and time-consuming, resulting in lengthy decision-making processes and increased susceptibility to risks. By automating these time-consuming procedures, decision engines usher in a paradigm shift. This automation not only saves cost, time, and effort in risk assessment and analysis, but it also improves the accuracy of risk predictions and assessments by 20–40% by utilising advanced algorithms and machine learning techniques. As a result, FIs are better equipped to make more informed decisions faster and with greater precision.

    Enabling Real-Time Risk Assessment

    The ability to perform real-time risk assessment is critical in the fast-paced world of Fintech and finance for quickly identifying and addressing potential risks. Decision engines excel in this area because they provide real-time insights into risk exposures. As abovementioned, these engines raise alerts quickly when predefined thresholds or anomalies are detected by continuously monitoring data. This proactive risk management approach enables organisations to take immediate action, thereby effectively preventing potential losses or fraudulent activities.

    Automating Compliance and Regulatory Requirements

    Compliance with regulatory frameworks and industry standards is a fundamental aspect of risk management in Fintech. By incorporating regulatory guidelines into their algorithms and workflows, decision engines prove invaluable in automating compliance processes. This ensures that risk assessments and decisions are in accordance with legal and regulatory requirements, effectively mitigating the risk of non-compliance and the resulting penalties. By streamlining compliance tasks, decision engines enable organisations to focus their efforts on strategic risk management initiatives.

    Challenges in Implementing Decision Engines

    While decision engines show great promise for improving risk management in the Fintech industry, successful implementation is difficult. To fully leverage the capabilities of decision engines in effectively managing risks, it is critical to address these challenges diligently.

    Data Quality and Integrity

    Decision engines place significant reliance on the accuracy and integrity of the data they process. Inaccurate, incomplete, or biased data can result in flawed risk assessments and decisions. To maintain data integrity throughout the decision-making process, companies must take proactive measures to ensure the availability of high-quality data and establish robust data governance practises.

    Interpretability and Explainability

    Given the complexity of decision engines’ algorithms and machine learning models, ensuring interpretability and explainability is critical. Fintech companies must have a thorough understanding of the factors that influence risk assessments and decisions generated by these engines. Transparent and explainable models foster trust among stakeholders, regulators, and customers, increasing decision engine acceptance and adoption.

    Best Practises for Utilising Decision Engines in Risk Management

    To fully realise the potential of decision engines and strengthen risk management practices, Fintech companies should implement the following best practices: 

    Ensuring High-Quality Data Inputs

    Fintech companies should prioritise investments in data quality management mechanisms to ensure that decision engines have accurate, trustworthy, and comprehensive data inputs. They should also maintain data integrity by continuously monitoring and authenticating data sources throughout the risk management lifecycle to identify opportunities for improvement.

    Establishing Robust Governance and Control Mechanisms

    Fintech companies must implement strong governance frameworks and control mechanisms to oversee the operation of decision engines. They should establish transparent accountability structures, clearly define roles and responsibilities, and evaluate the performance and effectiveness of decision models on a regular basis.

    Future Trends and Innovations in Decision Engines for Risk Management

    The field of decision engines is constantly evolving, and future trends are expected to improve risk management capabilities in Fintech. Some emerging trends include the incorporation of explainable artificial intelligence (XAI), the use of natural language processing (NLP) for data analysis, and the use of blockchain technology for secure and transparent decision-making processes.

    In a Nutshell

    Decision engines have transformed risk management in the Fintech industry. These advanced tools use AI, machine learning, and data analytics to enable organisations to make data-driven decisions, streamline processes, and improve risk assessment and mitigation strategies. However, for successful implementation, challenges related to data quality, privacy, security, and explainability must be addressed. Fintech companies can fully leverage the potential of decision engines by adhering to best practises and staying up-to-date on future trends.

    JurisTech, Your Preferred Partner

    JurisTech is a Fintech enabler that has a two-decade track record of success, specialising in credit management, digital banking, and AI. Our expertise in AI is demonstrated by our explainable automated ML and AI platform, which enables banks and FIs to make informed business decisions and gain valuable insights into real-world issues. Furthermore, by leveraging data-driven insights, our auto-decisioning engine enables businesses in a variety of industries to confidently and accurately make complex decisions in real-time. 

    Our technical experts and business consultants are dedicated to assisting clients in meeting their digital transformation objectives. We believe in building strong partnerships with our clients, tailoring solutions to their specific project requirements and organisational needs to ensure seamless integration with their systems. To ensure a smooth transition, we provide exceptional support that extends beyond project completion, including comprehensive post-implementation assistance and training to meet ongoing needs.

    If you would like to learn more about how JurisTech can help you achieve your digital transformation goals, please get in touch with us right away. Our team is always available to help you on your journey to a more efficient and effective digital future.


    About JurisTech

    JurisTech (Juris Technologies) is a leading Malaysian-based fintech company, specialising in enterprise-class software solutions for banks, financial institutions, and telecommunications companies in Malaysia, Southeast Asia, and beyond.


    By | 2024-01-17T17:50:30+00:00 23rd June, 2023|Artificial Intelligence, Fintech, Insights|

    About the Author:

    Ming Yih is a Marketing and Communications Executive at JurisTech. Ming Yih is a postgraduate with Master’s in Communications from Taylor’s University, with a strong sense of curiosity in the emerging fintech industry in Malaysia. Outside of work, he is a drummer, who graduated from the British and Irish Modern Music Institute of London.