Fast track to your first AI model implementation
With an AI Model Framework, you have the foundation for scaling AI across your organization. Our clients with growing AI ambitions for more advanced model development receive the support they need on the Grace Enterprise AI Platform.
AI Models have pre-built data pipelines based on specific data structures, all we need is your data.
AI Models come with the expertise from multiple similar model implementation, pre-packaged for easy AI implementation.
Packaged in the Grace Platform, AI Models are easily implemented in any IT infrastructure. Smooth integration with BI tools such as Qlik, PowerBI, or Tableau.
Analyzing past client lifecycles and the likelihood of churn
Churn is a challenge for many organizations. Implementing a model that analyzes past client life cycles and the likelihood of churn creates real business value. This way, organizations can be proactive and take action to reduce the risk of churn and offer insights into why a customer is likely to churn.
Managing information overload
Organizations are familiar with the large number of messages and information they can get on a daily basis. It is often a challenge for people within support and service functions to process and forward the right information to the right person. A way to solve this challenge is through message routing.
Prediction of interest payments
Determining repayment ability to prevent non-paying customers
For many credit giving institutions, it is essential to know which loan recipient is more creditworthy and who is risky. Credit classification predicts how likely a person is to pay interest on the loan. The solution will highlight factors that classify good credit users from risky ones.
Lowering acquisition costs and extending customer lifetime
Focusing on acquiring the right customers is paramount to ensure low acquisition costs and high customer lifetime. AI can help sort out which leads are worth spending time and efforts on, and thereby target the right customers.
Insurance claim rejection
Lower legal costs and insights into common claim issues
Claim management accounts for a large sum of costs for insurance companies. Insurance claim rejection models deliver an overview of the likelihood that a filed claim is at risk of being fraudulent, incomplete, etc., and needs to be rejected. This enables insurance companies to lower legal costs and get insights into common claim issues.
Efficient screening to classify fraudulent transactions
Financial fraud accounts for a significant amount of money globally. Knowing the likelihood of fraud in a transaction, as well as the drivers behind it, can enable the company to lower its rate of fraud through preventing transactions, or conduct more efficient screenings.
Determining the best price to stay competitive
Determining the right prices for products and services is a critical task for most companies. Price prediction models deliver an overview of the price which will be accepted by the market. Additionally, the company gets insight into which features drive the price for each product or service, enabling it to direct its product development efforts and price decisions.
Decrease response time to improve customer experience
Many companies still sort tickets manually, spending a lot of time on ticket distribution instead of resolving customer issues. With automated ticket sorting, companies decrease response time and improve customer experience, while freeing resources for customer assistance.
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Do you want to go from intellectual interest to actual integration and production of AI?