Contract Clause Recommendations Based on Historical Data

Use Case:

• Insurance providers often need to include customized clauses in policies, which are based on historical claims data and risk analysis.

Solution:

• NLP can analyze historical data stored in Microsoft Dynamics related to similar policies and suggest commonly used clauses or exclusions based on the client’s profile.
• Power Automate can then automatically insert the recommended clauses into the draft policy, allowing underwriters to focus on high-level reviews.

Benefit: Reduces manual work for underwriters, ensuring that policies are more comprehensive and tailored to individual risk profiles based on data insights.

The design of a monolithic system to exploit the possibilities of Generative AI using Power Automate and Microsoft Dynamics 365 Customer Engagement for recommendations on contract clauses involves bringing those technologies together into one coherent application.

In this architecture, all the components and services are firmly integrated into a single application, making deployment and management much easier but potentially less flexible than microservices.

Overall Structure for Monolithic System

1. Application Components

User Interface (UI)

Dynamics 365 CE is where the user will interact with it. A place where a user can insert data about the contract and request recommendations of clauses and show the results.

Custom UI Elements: Custom forms, dashboards, and views in Dynamics 365 CE specific to managing contracts and clause recommendations.

Business Logic Layer

Contract Clause Recommendation Engine: All the logic that will fire up for recommendations of clauses will lie here. This uses Generative AI to analyze historical data and make recommendations.

Power Automate Integration: Integrate the usage of Power Automate to trigger workflows and, where applicable, automation related to contract management and clause recommendations.

Data Access Layer

Historical Data Storage: Use Dynamics 365 CE entities to store historical data on contracts and clauses. This information would be used by the AI engine when generating recommendations.

Data Processing: Preprocess and cleansing mechanism to clean up the data before feeding it into the AI model.

Generative AI Integration

AI Model: to be implemented within the monolithic application to manage the creation of contract clauses based on historical data. This could involve direct API calls into Azure OpenAI or any other generative AI services.

Workflow and Automation

Power Automate Flows: to be implemented within the monolithic system for handling automation around data extraction, AI model invocation, and recommendation updates.

Data Storage

Centralized Database: In the case of SQL Server or Azure SQL Database, one database can persist all application data, user inputs, historic contracts, and AI-generated recommendations.

Security and Compliance

Authentication and Authorization: Include security measures that will ensure proper access by valid users to view and perform actions on sensitive data related to contracts.

Data Protection: Establish mechanisms for protection of data flow through encryption, secure storage, and most importantly, compliance with data protection regulations.

Monitoring and Logging

Application Monitoring: Health and performance monitoring for the monolithic application by using tools and services.

Logging: Logs capture user actions, AI model performance, and system errors for troubleshooting and auditing.

Detailed Workflow User Interaction: User interacts with the Dynamics 365 CE UI to provide information about the contract or request recommendations.

Trigger Automation: Flows in Power Automate are triggered due to a user interaction or scheduling tasks from the monolithic application.

Data Extraction and Processing:

Business Logic Layer: This will extract relevant historical data from Dynamics 365 CE and then pre-process it.

 AI Model Invocation: Generative AI model takes this historical data to do the processing and generate recommendations regarding contract clauses.

Results Integration: The AI-generated recommendations would get integrated into Dynamics 365 CE where users can view and apply.

User Review: Users will review those recommendations within the Dynamics 365 CE interface and implement them into their contracts.

Logging and Monitoring: This system logs interactions by users and performance of AI models, while monitoring tools track general application health.