The technology behind fintech success: decision engines in lending companies

Fintechs are reshaping financial markets by offering innovative, more accessible products to their customers. One of the reasons behind their success is the technology they use, especially when it comes to risk assessment and credit decision-making. In this article we look at how fintechs are deploying decision engines in their processes, and how lending companies can benefit from this machine-learning-based technology.

What is a decision engine?

A decision engine is software that uses algorithms and predefined rules to automate decision-making. At fintechs, these decision engines are used for credit risk assessment, allowing companies to make faster and more accurate calls based on the data available. Decision engines are particularly useful with high volumes of credit applications, since they can process and evaluate the information in a matter of seconds.

### See how uFlow's credit decision engine can improve risk management at your company

The uFlow decision engine gives you direct control over your credit policies through an intuitive, easy-to-use interface. With drag-and-drop functionality and familiar formula syntax, you can build visual, efficient workflows without giving up expressive power. uFlow also connects to different data sources, so you can analyze your company's transactional data through a dashboard with interactive filters.

Adding a credit bureau or any data provider in uFlow is straightforward: you simply select it in the editor toolbar and drag it to the point in the flow where the nodes sit inside the decision tree.

How do fintechs implement decision engines?

Fintechs combine data analysis and machine learning techniques to build custom decision trees tailored to their specific needs. Broadly, the implementation process involves the following steps:

* Define the assessment criteria: fintechs identify the key factors relevant to credit risk assessment, such as the applicant's credit history, current debt, cash flow, and so on. * Data collection: fintechs gather and process large volumes of data from multiple sources to feed their decision engines. * Model training: decision engines are trained using machine learning techniques to identify patterns and relationships in the data. * Validation and tuning: decision engines are validated and tuned based on the results obtained and on changes in market conditions.

### How can lending companies beyond fintechs implement decision engines?

Although fintechs were the first to adopt decision engines, traditional lending companies such as banks can benefit from this technology as well.

To implement a decision engine, financial companies should follow these 5 steps:

1. Assess your needs: companies should assess their specific needs and determine how a decision engine can improve their processes. 2. Select a provider: choose the decision engine provider that best fits those needs. 3. Integration: the selected decision engine has to be integrated into the company's existing processes. 4. Training: staff need to be trained to use the decision engine effectively. With uFlow, training runs across 3 sessions, all recorded so the customer can rewatch them as often as they want. 5. Monitoring and tuning: decision engines have to be monitored and tuned continuously to keep them effective and accurate.

In short, implementing a decision engine at a lending company is a strong strategy for improving decision-making efficiency and streamlining the risk assessment process.

The technology behind fintechs rests on innovative, effective solutions that let them compete in an increasingly demanding market. Implementing a decision engine is the best way to automate and streamline risk assessment, allowing lending companies to make more accurate, more efficient, and more profitable decisions. Adopting this technology not only improves the quality of the services a company offers, it also raises customer satisfaction and financial performance.