Who (or WHAT) Makes the Decisions Around Here? - Decisimo

Your loan just got approved in seconds. How? No human was sitting there, crunching numbers or thinking about your financial history. So, who—or what—made that decision? In today’s digital world, businesses rely more and more on automated decision engines to make critical choices instantly. This shift from human judgment to machine-driven decisions is changing industries, from finance to retail, and raises important questions about accuracy, speed, and accountability.

The Rise of Automated Decision Engines

Not long ago, business decisions—whether approving a loan, setting prices, or managing inventory—were slow and manual processes. For instance, a credit officer might spend hours reviewing an application, trying to balance policy with personal judgment. But companies like Amazon and Netflix showed the power of data-driven decisions, and the demand for automated decision engines took off. Today, these engines can process complex data in real-time, making decisions in seconds instead of hours.

Example: HSBC, one of the world's largest banks, implemented an automated decision engine to speed up its loan approval process. What used to take up to a week is now done in minutes, significantly boosting customer satisfaction and operational efficiency.

Understanding Automated Decision Engines

Automated decision engines are systems designed to automate decision-making by applying pre-defined rules and algorithms. These engines take in data, process it according to set criteria, and then output a decision. In the financial sector, for example, decision engines evaluate loan applications based on factors like credit score, income, and existing debt, instantly determining whether an applicant is approved or denied. Unlike human decision-makers, these engines aren't influenced by emotions or bias, ensuring consistent outcomes (What is a decision engine?).

As these systems advance, integrating generative AI is expanding what decision engines can do. Generative AI can understand and process unstructured data—like contracts, emails, and reports—and turn it into structured data that decision engines can use. This boosts decision accuracy, speed, and flexibility (quickly integrating decision engines).

Example: Walmart uses automated decision engines to manage inventory. The system analyzes real-time sales data, weather forecasts, and local events to decide when to restock items. If a heatwave is coming, the engine might automatically increase orders for ice cream and cold beverages, ensuring the store meets demand without overstocking.

Why Use a Decision Engine?

Businesses today are under pressure to make quick, accurate decisions while minimizing errors and staying compliant with regulations. Here are key reasons why decision engines are essential:

• Streamlined Decision-Making: Decision engines eliminate human errors and ensure every decision follows the same set of rules. For instance, when processing a credit application, the engine won’t miss a step or get swayed by emotions (learn more about JSONPath in decision engines).

• Faster Turnaround Times: Decision engines can process multiple transactions simultaneously, enabling instant responses to customers. This speed is crucial in sectors like finance, where quick decisions can make a significant difference.

• Error Reduction: Automated decision-making reduces the risk of costly mistakes by sticking to predefined rules without deviation.

• Rapid Adaptation: As business rules and regulations change, decision engines can be quickly updated to reflect these changes, ensuring businesses remain compliant and agile.

• Effortless Scaling: As your business grows, decision engines scale easily, handling increased volumes without the need for additional personnel or resources.

Example: Uber uses automated decision engines to set dynamic pricing for rides. The system looks at factors like current demand, traffic conditions, and the number of available drivers. During peak times, like rush hour, the decision engine automatically raises prices to encourage more drivers to get on the road, balancing supply and demand efficiently.

Integrating Alternative Data in Decision Engines

In the past, decision engines relied on a single data source or a simple call to a credit bureau. Today, the process is far more sophisticated. Modern decision engines can make 20 to 50 calls to various microservices and external data providers, pulling in alternative data such as (external data sources):

• Transaction Histories: Real-time data on purchases and payments.

• Social Media Activity: Insights into behavior and lifestyle.

• Geolocation Information: Patterns of movement and location-based behaviors.

This broader data spectrum allows for:

• More Accurate Credit Scoring: Incorporating non-traditional data sources (alternative data in credit scoring).

• Nuanced Decision-Making: Especially in industries like insurance and finance.

• Adapting to Market Changes: Quickly responding to new consumer behaviors (securing app credit lending BNPL).

Example: LendingClub, a financial company, uses alternative data like mobile payment history and social media activity to assess creditworthiness. This approach has allowed them to extend credit to individuals who might not have traditional credit scores but demonstrate reliable financial behavior through other means.

Types of Automated Decision Engines

There are various types of decision engines available, each suited to different business needs:

• Traditional Solutions: Platforms like FICO Blaze Advisor have been industry standards for years, especially in finance. These systems are powerful but often come with high costs and require specialized expertise to manage.

• Open-Source Solutions: Tools like Drools offer flexibility and customization, making them popular among tech-savvy companies. However, their complexity can be a barrier for businesses without deep technical resources.

• Cloud-Based Solutions: Modern platforms like Decisimo provide scalability, ease of use, and quick deployment. These systems are particularly attractive to businesses that need to integrate decision engines into their operations quickly and without significant upfront investment (simplify ML deployment with Decisimo).

The Advantages of Automated Decision Engines

• Consistency and Accuracy: Automated decision engines consistently apply rules without deviation. For instance, a bank using an engine like Decisimo ensures that every loan application is evaluated under the same criteria, reducing the risk of human error. Moreover, compliance with regulations like the EU AI Act, which demands transparency and accountability in AI-driven systems, further enhances the reliability of these engines.

• Speed and Efficiency: Automated decision engines process data and make decisions in seconds, something a human could never achieve. This speed is crucial in industries where time is money. For example, e-commerce companies use decision engines to adjust prices in real time based on demand, inventory levels, and competitor pricing, optimizing revenue instantly.

• Flexibility and Adaptability: Business environments change rapidly, and so must the criteria used in decision-making. Platforms like Decisimo allow businesses to update decision rules quickly, ensuring they can adapt to market changes or new regulations like DORA, which mandates strong ICT risk management and operational resilience in the financial sector.

Integrating Generative AI into Automated Decision Engines

Generative AI is adding a new dimension to decision-making by enabling engines to process and understand unstructured data. This capability transforms documents, emails, and reports into actionable data, enhancing the effectiveness of decision engines (enhanced decision-making).

Example: In the insurance industry, companies like Lemonade use generative AI to process claims. When a customer submits a claim, the AI reads and interprets the claim form and any attached documents, such as photos or receipts, then cross-references this information with the customer's policy to make an instant payout decision. This reduces processing time from days to mere seconds.

The Importance of Regulatory Compliance

As decision engines become more integral to business operations, staying compliant with emerging regulations is critical. The EU AI Act categorizes AI systems into risk levels and imposes strict requirements on high-risk applications, such as those used in financial services. Companies using AI-driven decision engines must ensure that their systems are transparent, accountable, and safe (evaluating ROI in financial companies).

Similarly, the Digital Operational Resilience Act (DORA) requires financial entities and their ICT providers to maintain high levels of operational resilience. This includes robust ICT risk management, incident reporting, and regular testing to ensure systems can withstand and recover from disruptions. For decision engines, this means incorporating features that not only comply with these regulations but also enhance overall system resilience (decision engine vs. spreadsheet).

Example: Deutsche Bank, a multinational financial institution, uses automated decision engines that are designed to be both transparent and capable of rigorous testing. This ensures that the bank meets regulatory requirements while also maintaining operational resilience, reducing the risk of fines or disruptions.

Making the Right Choice for Your Business

When choosing a decision engine, businesses must consider several factors: the complexity of their operations, regulatory requirements, and long-term scalability. Here are some options:

• Traditional Solutions: Powerful but may be overkill for smaller firms.

• Open-Source Platforms: Offer customization but require significant technical expertise (why you need a decision engine if you have BRMS).

• Cloud-Based Solutions: Balance advanced features with ease of use and quick deployment (5 components of rule engine architecture).

Example: A mid-sized e-commerce company might choose a cloud-based solution like Decisimo to manage its automated pricing strategy. With a small IT team, the company needs a system that’s easy to implement and doesn’t require extensive customization, making a cloud-based platform the perfect fit for quick deployment and scalability as the business grows.

Future-Proofing Your Decision Engine

With regulations like the EU AI Act and DORA on the horizon, businesses must ensure their decision engines are not only effective today but also ready for the future. This involves selecting platforms that are easily updatable, compliant with current laws, and adaptable to new challenges (the evolution of decision making).

Key Considerations for Future-Proofing:

• Explainability: As regulators like those enforcing the EU AI Act require transparency, businesses must choose decision engines that can explain how decisions are made. This is crucial not only for regulatory compliance but also for building trust with customers and stakeholders.

• Approval Processes for Changes: Given the significant fines for non-compliance under DORA, any changes to decision-making strategies should follow a traceable approval process. Before pushing a new strategy live, it must undergo rigorous testing to ensure alignment with regulatory standards. This helps in avoiding penalties and ensures that all changes are documented and auditable, providing clear records for both internal audits and external regulatory reviews.

Example: Tesla has invested heavily in decision engines that can be easily updated and integrated with new technologies. As regulations around autonomous driving evolve, Tesla's decision engines are designed to be adaptable, ensuring that their vehicles remain compliant with both current and future laws while continuing to innovate.

Conclusion

Automated decision engines are changing how businesses make decisions, offering speed, accuracy, and consistency that human decision-makers can't match. However, as these systems become more widespread, it’s crucial to choose platforms that not only meet your business needs but also comply with evolving regulations. By integrating generative AI and ensuring compliance with frameworks like the EU AI Act and DORA, businesses can maintain a competitive edge and ensure long-term operational resilience.