Why are large resources invested in the transformation of the collection?
Collection is definitely not the most attractive part of the credit system. And not the most pleasant one for both sides of the process. But it is absolutely necessary - the credit system cannot exist without it. That is why now large resources are being invested in its transformation. The goal is to make the process less stressful for both parties and increase its efficiency.
Both banks, MFOs, collectors and startups are working on solving the problem. TrueAccord (USA), Attunely (USA), Dasceq (USA), Symend (Canada), CollectAI (Germany), inDebted (Australia), Ziyitong (China), Flow (Singapore) and others are transforming the collection through innovative approaches. And at the heart of all of these approaches are big data and machine learning.
Having studied in more detail what exactly these startups are doing, I have identified 3 main areas for the application of big data and machine learning in the collection industry.
Additional Information.
The Chinese company Ziyitong has managed to recover about $ 29 billion in debt since it opened in 2016. Its clients include about 600 debt collection agencies and more than 200 creditors. The company is based on a machine learning system that includes two main elements.
The first is the collection of data about the borrower and his environment from open sources (mainly social networks). The system analyzes the information found, and then uses it in telephone contacts with the borrower and his friends.
The second is the personalization of communication with the borrower. The system records the conversations, analyzes them, and then determines the wording that is most likely to compel the borrower to repay the debt.
The return rate of such a system is 41%, which is on average 2 times higher than that of traditional collection methods in China (20%).
Personalization of time, return conditions, frequency and communication channel
This is the most popular area, on the development of which all of the above startups are working.
So, TrueAccord is trying to soften the collection process for borrowers as much as possible. To do this, the company has completely abandoned phone calls and sends emails to borrowers with personal return conditions, and reminds them about them using text messages and targeted Facebook ads.
CollectAI, a German startup, is also looking at message sending times, email content and opening conversions, which helps it create dynamic landing pages for each individual borrower. And these actions are yielding results - within the framework of a 6-month cooperation with Hanseatic Bank, the bank's overdue debt recovery rate increased by 24%.
Personalization of messages.
From the whole area of personalization of communication with the borrower, it is worth highlighting the personalization of messages, which is used by most of the above startups. It is technically the most difficult element, but at the same time it is the most effective. To implement it, you must complete the following steps:
Collection is definitely not the most attractive part of the credit system. And not the most pleasant one for both sides of the process. But it is absolutely necessary - the credit system cannot exist without it. That is why now large resources are being invested in its transformation. The goal is to make the process less stressful for both parties and increase its efficiency.
Both banks, MFOs, collectors and startups are working on solving the problem. TrueAccord (USA), Attunely (USA), Dasceq (USA), Symend (Canada), CollectAI (Germany), inDebted (Australia), Ziyitong (China), Flow (Singapore) and others are transforming the collection through innovative approaches. And at the heart of all of these approaches are big data and machine learning.
Having studied in more detail what exactly these startups are doing, I have identified 3 main areas for the application of big data and machine learning in the collection industry.
Additional Information.
The Chinese company Ziyitong has managed to recover about $ 29 billion in debt since it opened in 2016. Its clients include about 600 debt collection agencies and more than 200 creditors. The company is based on a machine learning system that includes two main elements.
The first is the collection of data about the borrower and his environment from open sources (mainly social networks). The system analyzes the information found, and then uses it in telephone contacts with the borrower and his friends.
The second is the personalization of communication with the borrower. The system records the conversations, analyzes them, and then determines the wording that is most likely to compel the borrower to repay the debt.
The return rate of such a system is 41%, which is on average 2 times higher than that of traditional collection methods in China (20%).
Personalization of time, return conditions, frequency and communication channel
This is the most popular area, on the development of which all of the above startups are working.
So, TrueAccord is trying to soften the collection process for borrowers as much as possible. To do this, the company has completely abandoned phone calls and sends emails to borrowers with personal return conditions, and reminds them about them using text messages and targeted Facebook ads.
CollectAI, a German startup, is also looking at message sending times, email content and opening conversions, which helps it create dynamic landing pages for each individual borrower. And these actions are yielding results - within the framework of a 6-month cooperation with Hanseatic Bank, the bank's overdue debt recovery rate increased by 24%.
Personalization of messages.
From the whole area of personalization of communication with the borrower, it is worth highlighting the personalization of messages, which is used by most of the above startups. It is technically the most difficult element, but at the same time it is the most effective. To implement it, you must complete the following steps:
- start recording all conversations with borrowers, track their effectiveness;
- use an audio transcription system and with its help translate telephone conversations into text;
- compare 3 types of data with each other: the type of the borrower, the text of telephone conversations, efficiency;
- to build the type of the borrower, collect external data (first of all, information from social networks);
- on the basis of 3 types of data, train an algorithm that predicts the optimal messages for each type of borrower.