Our cases
- Industry
- Areas of AI
- Capabilities
Predicting a company’s future financial performance
Modulai built a deep learning based system for predicting the values of various fields in a company's annual report - a year in the future.
Currently, there exist ML models to predict different financial KPIs such as debt, profits, revenue, etc. of a company based on their historic values. The values of these fields are related and dependent on each other.
Bayesian hierarchical model for company credit risk
We developed a credit system to assess the credit risk associated with small to medium-sized enterprise (SME) lending.
The client is an American software company that provides solutions to financial institutions. Their offer includes technology for compliance, credit risk, and lending solutions used to manage risk.
Transaction risk model for a major online payment platform
A team from Modulai trained a model to predict risk direct banking transactions. The project included a full specification of the decision engine infrastructure as well as the deployment plan.
This client is a large fintech in the payments space providing an alternative to credit cards. They process many millions of transactions yearly for thousands of e-commerce sites and online service providers. Each transaction might expose the different parties to various risks, and containing these risks are crucial.
Predicting the risk of mission failure in shipments of pharmaceuticals
We applied a set of machine learning techniques to predict the risk of shipment-failure in shipments of temperature-sensitive pharmaceutical goods.
This client is one of the world’s leading providers of solutions for tracking sensitive pharmaceutical goods during shipment and throughout the whole supply chain. Each shipment is tracked minute-by-minute by a temperature logger.
Intelligent indoor climate solution for real estate developer
Predicting what the temperature will be in a meeting room, 25 minutes into the future.
We used data that had been gathered over a year at the company’s HQ. A combination of temperature, CO2, outdoor temperature, climate system, and meeting room booking data.
Predicting when rented equipment will be returned
As one of the largest rental equipment providers in the Nordics and suppliers to thousands of construction sites, this client needed a system capable of predicting when lent out equipment was expected to be returned.
The goal was to optimize the vast logistics chain by minimizing unnecessary transports between their local shops and main warehouses.
Detecting underlying paroxysmal atrial fibrillation for Zenicor
Together with Zenicor Medical Systems AB, we trained a deep learning model to predict an underlying paroxysmal atrial fibrillation (AF) condition in patients.
Every day, thousands of people suffer from stroke due to an underlying undiagnosed AF condition. Stroke often results in conditions such as paralysis or, in many cases, death. For this reason, precise and early diagnosis of AF is an essential part of stroke prevention.
Detecting food intake in glucose time-series from diabetes patients
Together with Digital Diabetes Analytics, we created a meal detection system using glucose level data to help people with insulin-treated diabetes.
Today, more than 5,5 million people worldwide live with type 1 diabetes and need insulin-based treatment to survive. When it comes to diabetes, there’s no “one treatment fits all”, therefore doctor’s need support in optimizing treatments for each patient.
Forecasting the success of fishing trips, for millions of Fishbrain users
Data from 2.5 millions catches was collected and refined. Each data point was annotated with date, time, location, and fish species. Detailed weather information was added to each catch; air temperature, air pressure, wind speed, cloud cover, and precipitation. Temporal data was transformed into astronomical conditions such as moon phase, solar irradiation, and azimuth angle. A global climate model was created,
