Predict Customer Defaults of Online Loans Thanks to Data Enrichment

A closer look at fraud cases in the online lending industry, and ways to protect your business.

In this webinar, we are focusing on online lending. The aim is to present the fraud cases most often faced by this industry such as identity theft and synthetic identity thefts. We will explain how fraud prevention solutions can help fine-tuning credit scoring by adding thousands of additional data points to an existing model. We will then show how this can help predict future defaulting customers. 

We would like to find that fine line where we can offer relevant information for fraud and IT specialists, but keeping it conversational so managers and business developers who are new to the topic can gain an understanding as well.

Agenda

Trends in the industry

  • Facts and figures (growth of the market, mobile phone bringing underbanked masses into the business scope, fraud losses in the industry, fintech vs traditional banking).

Easy use and application vs solid customer identification.

  • Balancing friction and security to remain competitive while still keeping fraud and defaulting customers out. 

Leveraging existing company data.

  • The challenge isn’t to gather more data, but to make sense of what’s already there.

Data enrichment in real-time

  • An interactive demo.

Customer success story

  • What data proved to be a good prediction of future default? 

 

Watch our webinar to learn about how to predict customer defaults of online loans with data enrichment.

You might also be interested in reading about:

Learn more about:

Data Enrichment | Browser Fingerprinting | Device Fingerprinting | Fraud Detection API

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Author avatar
Bence Jendruszak
COO

Bence is the co-founder and COO of SEON whose vision is to create a safer online environment for merchants in high risk verticals.


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