As technology evolves, so do credit risk management tools. Yet, despite AI and machine learning, many lenders still cling to manual risk assessment tools, spreadsheets, and outdated strategies. The fundamental advantage to new credit risk assessment tools lies in the power of making the right decisions in a constantly changing world and, also, in being able to supervise client applications and manage different portfolios.

Informed decisions through alternative scoring

Through the use of metadata, new alternative scoring models can now obtain more accurate credit profiles.

The alternative data used for these credit ratings is information not strictly related to the customer's credit behaviour but is obtained from non-traditional sources such as digital platforms. Some examples include telephone bill payments, taxes, electricity, gas, rentals, online purchases, among others.

In this way, by being able to complement traditional information with other data sources, it is possible to obtain a much more detailed picture of the creditworthiness of a potential borrower. This further allows providers to offer more personalized and, therefore, more effective products.

These complex data sets require powerful technologies for their correct analysis and evaluation. In this sense, artificial intelligence makes it possible to carry out non-linear prediction models to find ever deeper insights. Some of the advantages of using AI for credit scoring include:
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• Making improved future predictions about customers' ability and willingness to pay based on current data.
• Identifying patterns that drive the client's financial activities.
• Building the financial behaviour profile of the client in various circumstances.

Ultimately, these innovations allow companies to make firm strides, expanding their reach and recognizing profitable loan opportunities.

Artificial intelligence to manage applications

Reducing credit risk involves verifying information and requires lenders to manage their applications and portfolios effectively. For example, if an account is delinquent, lenders must take necessary action. Automation and machine learning allows to:
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• Eliminate high-risk applicants in minutes.
• Structure loans based on projected and past risk management data.
• Prioritize high-risk and low-risk loan applications.
• Automatically approve very low-risk applicants.
• Automate the debt recovery and collection process to reduce financial risk.
• Identify patterns and create new decision rules based on these trends.

New technologies that combine alternative information and artificial intelligence make it possible to reduce risks at all stages of credit processing quickly. With connected data, you can assess risk and forge decisions to grow your business and prevent vulnerabilities.

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