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Project

Alfa-Leasing introduced artificial intelligence to optimize sales of leasing services

Customers: Alpha Leasing (Alfamobil)

Moscow; Financial Services, Investments and Auditing

Product: WaveAccess ValueAI

Project date: 2024/01  - 2024/07

2024: Introduction of AI to optimize sales of leasing services

On July 16, 2024, Alfa-Leasing Group of Companies announced that, together with the IT company WeivAxess, it had developed a system for predicting leasing needs based on artificial intelligence. The solution, implemented on the basis of the Russian ValueAI platform, allows you to determine contacts that are ready to re-purchase leasing products, predict the subject of leasing and predict new customers.

source = Alpha Leasing
Alfa-Leasing introduced artificial intelligence to optimize sales of leasing services

According to the company, the WeivAxess team processed historical data and built a model using algorithmic machine learning. The model highlights and recommends to call contacts that with are most likely to make a deal soon. The solution is deployed in the infrastructure of Alfa-Leasing, which allows you to control and control data. safety

A separate model in the system is responsible for predicting the most suitable leasing item (passenger, cars cargo or special equipment). Also, the decision is able to allocate organizations that have not previously concluded lease agreements, but potentially have such a request. The AI model provides a list of all promising customers, data is used to establish contact and select an address offer.

The solution based on ValueAI makes a forecast automatically, monthly receiving data from the Unified Federal Register of Information on the Facts of the Activities of Legal Entities, as well as from the counterparty verification system - in the case of new clients. It is also possible for Alfa-Leasing specialists to quickly analyze individual samples on request, loading data manually.

The AI model processes the database into more than 400,000 customers and thousands of contracts each month. According to the latest results, the decision predicted interest in a second deal in 14,197 contacts. Of these, 3,240 were qualified by Alfa Leasing as "leads" and hired by sales managers - 15.5% entered into a deal with Alfa Leasing. At the same time, almost 50% of the recommended organizations subsequently signed an agreement with one of the leasing companies.

{{quote 'author
= shared by Alexander Vorobyov, leader Management of Strategic Design and Digital Innovation of Alfa-Leasing Group "Thanks to the joint work of internal teams of RnD, corporate storage of Alfa-Leasing Group and the WaveAxess team, we were able to quickly conduct a pilot, assess its effectiveness and begin to expand the capabilities of AI in our company. At the same time, not only within the framework of the leasing sale direction, but also working with the assessment of the client's risk profile, communication tools, and so on.}}

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The leasing services market is finite and is growing less dynamically as of July 2024. It is increasingly difficult for leasing companies to expand rapidly due to new customers. Therefore, an AI-based solution that predicts the leasing needs of potential customers is so relevant to the industry. We have done a lot of work with the quality of the data, as well as the search for available resources. In partnership with WaveAxess, it was possible to quickly conduct a pilot on historical data and show business colleagues the possible effectiveness of the model. The project was perceived positively and thanks to the decision of the Smart Sales division, we have built a predictive model into the company's processes.

noted Artem Kosolapov, Information Technology Director of Alfa-Leasing Group of Companies
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Due to timely communication with promising contacts, the efficiency of cold calls has doubled. Alfa-Leasing Group optimized the performance of the upsale and increased the average check. The use of the ValueAI platform in the project also made it possible to optimize the introduction of artificial intelligence into processes by 40%.

The project development plans include expanding the list of data sources to optimize the operation of the existing AI model.