We are a technology company that uses the most modern Machine Learning tools to help companies solve problems. Using predictive models for data analysis, we are able to operate in different verticals to optimize processes, business formats and value chains. We make the decision process of our customers faster, more agile and simpler.
"Em parceria com nosso time de Dados, os especialistas da Datarisk trouxeram excelentes insights e expertise que reforçaram nossa avaliação de risco e prevenção à fraude. A complementação do conhecimento das equipes resultou numa solução que nos ajudou a entender ainda melhor o perfil de cada cliente e entregarmos produtos mais personalizados e atrativos ao mercado, diminuindo burocracias e oferecendo taxas mais justas."
“A ferramenta é ótima e não temos dúvidas sobre a capacidade técnica de quem está por trás. Mas acima de tudo a parceria entre as empresas é muito boa. A proximidade e a comunicação são sem travas e toda sugestão que levamos é considerada, toda dúvida que temos é sanada em um prazo muito rápido.”
“Encontramos na Datarisk uma solução rápida e eficiente para nossa gestão de crédito. Desenvolvemos um algoritmo de machine learning para reduzir o risco de inadimplência nas vendas que realizamos semanalmente a mais de 200 mil clientes espalhados por todo o Brasil. Fomos surpreendidos com a quantidade de insights gerados pelo algoritmo e já vemos impactos positivos na nossa operação.“
A player in the industrial sector faced the challenge of finding a solution to reduce nonpayment and boost sales.
Datarisk's solution was a policy of granting credit based on the sales history of the POS (Point of Sale).
This resulted in a significant improvement in customer credit analysis, increasing sales and reducing nonpayment by 30%.
A large international company had a slow manual credit granting process for verifying business information. It took, on average, 4 days to reach an opinion on the analysis.
Datarisk's solution was to create a credit approval cycle using data from past purchases, extracting balance sheet data in pdf format and credit bureau information.
As a result, we created a new credit policy, standardized the approval rules and the analysis started taking less than 1 minute.
Suvinil, a major Brazilian chemical manufacturer, needed to improve demand forecasting for all its products from each distribution center.
Datarisk's solution was to build a collaborative platform to adjust demand forecasts with all areas involved. Hence, we developed a model using internal data and external indicators to forecast demand by product and distribution center.
The result was a 10% improvement in forecast accuracy in the backtest performed and automation of demand forecasting by the regional sales team.
Suzano, a large Brazilian paper and cellulose manufacturer, needed to reduce the frequency with which its products were out of stock on the shelves of partner supermarkets.
Datarisk's solution was to develop a stockout prediction model that informs the best date for the consultant to contact the partner to place orders, Identify the problem in the distribution chain that was influencing the final problem and suggest the volume of "ideal order" to serve customers.
As a result, we had a reduction of almost 50% of the rupture in the backtest performed.
The lack of visibility of what caused defects in the manufacturing of parts was one of the main problems for a player in the rubber industry, with a high proportion of parts with manufacturing failures.
The solution that Datarisk brought was the creation of predictive models to identify which were the best machine configurations in the production of each part.
The result was more than a 50% reduction in defective parts.
With the isolation resulting from the pandemic, a multinational pharmaceutical company felt the need to increase communication with doctors due to low email marketing conversion.
Datarisk's solution was to create different models to understand: the best way and time to communicate, the profile of engaged physicians and the best email format to be applied.
As a result, we had a 35% increase in the open and click rate of emails.
Learn how to make the best decisions and follow tips on how to optimize existing processes.
Our CEO Jhonata Emerick presents an interesting point of view on the evolutionary aspects of statistical models.
Learn how we optimized the product sales process for the distribution center and teams for a quarter and the following year.
