Solutions for Your Industry
Banking & Financial Services
Financial institutions are increasingly using AI and machine learning in a range of applications across the financial system including to assess credit quality, to price and market insurance contracts and to automate client interaction. Institutions are optimising scarce capital with AI and machine learning techniques, as well as back-testing models and analysing the market impact of trading large positions. Meanwhile, hedge funds, broker-dealers and other firms are using it to find signals for higher uncorrelated returns and to optimise trade execution. Both public and private sector institutions may use these technologies for regulatory compliance, surveillance, data quality assessment and fraud detection.
Modules: Churn modelLing, Fraud detection
AI in agriculture is emerging in three major categories, (i) agricultural robotics, (ii) soil and crop monitoring, and (iii) predictive analytics. Farmers are increasingly using sensors and soil sampling to gather data and this data is stored on farm management systems that allow for better processing and analysis. The availability of this data and other related data is paving a way to deploy AI in agriculture. We are seeing, as a result, a number of tech companies investing in algorithms that are becoming useful in agriculture. For example, we have image recognition used in potatoes, by a Georgia-based startup for using natural language toolkit for field notes, and yield prediction algorithms based on satellite imagery
Modules: Image recognition*, Sales optimization
AI is a boon for retailers seeking to accurately predict demand, anticipate customer behavior, and optimize customer experiences. 30% of major retailers will adopt a retail omnichannel commerce platform that integrates a data analytics layer that centrally orchestrates omnichannel capabilities. According to Mckinsey Retailer supply chain operations that have adopted data and analytics have seen up to a 19% increase in operating margin over the last five years. Personalizing advertising is one of the strongest use cases for machine learning today. Additional retail use cases with high potential include optimizing pricing, routing, and scheduling based on real-time data in travel and logistics, as well as optimizing merchandising strategies.
Modules: Sales optimisation, Churn modeLling, Recommender systems
Energy & Utilities
Utilities, like every other industry, can stand to benefit from the innovation now being unleashed by new technologies. With the data collected from smart meters through IoT, utilities are offering not only enhanced customer service but are providing entirely new services like energy efficiency advising. Using sensors, customers are monitoring home temperatures and energy use. Predictive analytics enabled by AI is helping leading enterprises optimize operational efficiency in fixed assets, such as grid operations and improving the overall performance of the utility.
Modules: Sales optimization, Churn modelling, Recommender systems
AI in health represents a collection of multiple technologies enabling machines to sense, comprehend, act and learn so they can perform administrative and clinical healthcare functions. Growth in the AI health market is expected to reach $6.6 billion by 2021—that’s a compound annual growth rate of 40 percent.
Modules: Image recognition*, cancer detection, Recommender systems