Streamlining Insurance Projects with AI-Powered Backlogs: EPAM Project Backlog Generator

An AI-powered project management accelerator, EPAM's project backlog generator, provides a set of proven prompts that can be customized to address specific business challenges, objectives, personas to create a backlog of projects/initiatives to significantly reduce the time needed to initiate a new development project.  This is changing how insurance development teams tackle a longstanding challenge: the slow and laborious process of generating project backlogs. These crucial roadmaps outline upcoming tasks and priorities and can take weeks to compile manually. This time-consuming limitation restricts efficiency and delays to project execution, slowing insurance development teams in today's fast-paced environment.

To overcome this limitation, Artificial Intelligence (AI) can offer many benefits: faster project initiation and execution, reduced costs, and a significant boost in overall productivity for insurance development teams.

Use Cases

Let's delve into how AI can help carriers with efficient backlog management in four key areas:

1. Insurance Onboarding, Distribution, and Sales

Challenge: The insurance sales and distribution process involves complex tasks—integrating with multiple channels (agents, brokers, online platforms), managing customer data, tailoring quotes, and ensuring regulatory compliance. Creating backlogs for optimizing these processes can be cumbersome, time-consuming, and prone to errors.

Solution: Using an AI-powered backlog generator that can be customized to analyze customer data and past sales trends, integrating new distribution channels to expand reach, automating data pre-filling and quote generation to improve efficiency are just a few examples of how the Accelerated Discovery, backlog generator can be extended to apply comprehensive value. The result is greater value with each project initiated where a carrier’s position can be elevated in the market.

Achieved results:

• Faster time to market: By analyzing trends and generating backlogs focused on new integrations and improved processes, AI helps carriers launch new products and features faster.

• Enhanced customer experience: Automating tasks and personalizing the quote experience through intelligent backlogs leads to a smoother and more efficient customer onboarding journey.

• Improved operational efficiency: AI optimizes tasks and automates repetitive steps, allowing carrier teams to focus on strategic initiatives and customer interaction, leading to overall efficiency gains.

2. Underwriting (UW)

Challenge: Underwriting is a data-driven process that requires risk assessment, policy pricing, and fraud detection. Traditional backlog-creation methods struggle to keep up with the ever-increasing volume of data and changing risk factors. This can lead to policy issuance delays and missed opportunities.

Solution: Extending the use of an AI-powered backlog generator that can also analyze vast datasets and identifies patterns in risk factors and claims history can address this challenges quickly. AI can also support the rapid project backlog with integrated AI-powered risk assessment models to improve accuracy and efficiency at scale, while also embedding automated fraud detection algorithms based on suspicious claims patterns are also top of mind for Underwriting leaders.

Achieved results:

• Improved underwriting accuracy: AI analyzes vast datasets and generates backlogs focused on optimizing risk assessment and fraud detection, leading to more accurate pricing and faster underwriting decisions.

• Streamlined workflow: Backlogs tailored to automate tasks like data gathering and document verification allow underwriters to focus on complex risk analysis and customer interaction, improving overall workflow efficiency.

• Enhanced regulatory compliance: Machine learning-driven backlogs can ensure underwriting processes remain compliant with evolving regulations, minimizing risks and penalties for carriers.

3. Policy Servicing

Challenge: Policy servicing is known for managing policy renewals, handling customer inquiries, processing payments, and resolving claims. With each of these engagement opportunities, applying value to the policyholder is highly important.

Solution: Applying an AI-driven backlog creation capability, which can integrate past customer interactions and service requests means that data-driven value is applied for each step of this value chain. If you are focused on developing chatbots or virtual assistants to answer frequently asked questions and provide initial support, building self-service portals for customers to manage their policies and payments online, the ability to gain awareness of external and internal trends is valuable and included into the resulting backlog.

Achieved results:

• Improved customer service: Backlogs focused on automation through AI free up customer service representatives to handle complex inquiries and provide personalized support, leading to higher customer satisfaction.

• Reduced operational costs: AI automates repetitive tasks, minimizing errors and rework and lowering carrier operational costs.

• Enhanced customer retention: Improved policy servicing processes and efficient customer support, facilitated by AI-powered backlogs, contribute to higher customer satisfaction and retention rates.

4. Claims

Challenge: Claims processing involves gathering information, assessing damage, negotiating settlements, and managing payouts. First Notice of Loss (FNOL) information and data are provided by the customer, but can result in inconsistencies and unrecognizable patterns for the carrier processing these claims.

Solution: The use of an extendable AI series of prompts to generate a backlog with intelligence that can also analyze past claims data, identify fraudulent claims, and process delay patterns results in a comprehensive claims project outcome. The ability to gain an efficient claims processing, faster decisioning and payouts and higher customer satisfaction. All of this can be possible with the extension of our backlog generator prompt series that can include internal and external data analysis.

Achieved results:

• Faster payouts: Improving tasks, speeding up processing and claim settlements.

• Reduced fraud: Identifying suspicious patterns and minimizing fraudulent claims payouts.

• Improved accuracy: Automating tasks, reducing errors, and ensuring accurate assessments.

• Happier customers: Faster payouts, less fraud, and accurate assessments increase customer satisfaction.

EPAM's AI-Powered Backlog Generator: Supercharge Your Insurance Projects

EPAM's Accelerated Discovery (AD) is an extendable set of proven prompts that insurance development teams can leverage to streamline project backlog generation. This AI-powered tool can include the analysis of vast data sets (internal & external) with industry trends to generate comprehensive project backlogs at scale. It considers core details on insurance topics available with any open source LLM’s and suggests project approaches with insights for innovation, ensuring your backlogs are relevant and future-proof.

Faster Project Initiation and Execution

With EPAM's project backlog management system, teams can significantly reduce backlog creation time (up to 60%), allowing for faster project initiation and capitalizing on new opportunities.

Improved Collaboration and Stakeholder Engagement

Effective project management hinges on collaboration. AI-powered backlog generation tools like AD facilitate seamless communication and cooperation among team members, stakeholders, and subject matter experts. Producing a project backlog faster, means Business Analysts can engage with SME’s and project teams to refine the backlog vs the lengthy times producing them.  This ensures everyone is aligned and can contribute, enabling a more inclusive and collaborative planning process, faster.

Reduced Costs and Optimized Resources

The time and effort saved through AI, lead directly to cost savings. Businesses can reduce project management costs by up to 25% using AD. Additionally, AI automates repetitive tasks and optimizes resource allocation, enabling teams to maximize project managers' productivity, leading to more efficient use of human resources.

Increased Innovation

While some AI backlog tools automate tasks, AD goes a step further. Leveraging open source LLM’s, these prompts can use advanced analytics capabilities to identify trends and suggest innovative approaches based on industry insights. This empowers development teams to complete tasks and identify and implement new ideas that drive growth and a competitive edge.

Future-proofed Backlogs

The insurance industry is constantly evolving. AD doesn't just create backlogs for today. It considers industry trends. This ensures your backlogs are relevant, address upcoming challenges, and position your team for long-term success.

Quantifiable Benefits: A Case Study for Insurance Development Teams

Let's consider a hypothetical scenario to illustrate the tangible benefits:

Traditional backlog creation:

• Time per project: 40 hours

• Project management costs: $80,000/year

• Projects initiated annually: 25

Calculations:

• Annual time spent: 40 hours/project * 25 projects = 1,000 hours

• Annual cost: (1,000 hours / 2,080 working hours/year) * $80,000 = $38,461

AI-powered backlog creation:

• Time per project: 16 hours (60% reduction)

• Project management costs: $80,000/year

• Projects initiated annually: 25

Calculations:

• Annual time spent: 16 hours/project * 25 projects = 400 hours

• Annual cost: (400 hours / 2,080 working hours/year) * $80,000 = $15,385

Savings:

• Time saved annually: 1,000 hours - 400 hours = 600 hours

• Cost savings annually: $38,461 - $15,385 = $23,076

In this scenario, the insurance development team could save 600 hours and $23,076 annually by adopting AI-powered backlog generation. These savings can be reinvested in product development, marketing, or employee training, further propelling the company's growth.

Implementing Accelerated Discovery for Carrier Needs

Let's explore how Accelerated Discovery integrates seamlessly with existing project management software while enhancing the quality of your carrier-specific backlog.

Seamless Integration

One of AD's key strengths is its ability to integrate flawlessly with existing project management software. Popular platforms like Jira and Trello offer third-party applications and plugin integration options. This integration streamlines workflows by eliminating manual data entry and duplication of efforts.

In essence, AD becomes an extension of your chosen project management tool, boosting efficiency and ensuring everyone on the team is always on the same page.

"We are excited to see EPAM continue to lead the pack with AI for Insurance with Accelerated Discovery. The ability to infuse rapid acceleration of projects results in speed-to-market for Development teams in the industry. The pace of Insurance has just dialed up a notch and we are proud to be the catalyst of this innovative change."

Eric Fenton, Principal, Insurance Business Consulting EPAM Systems

Enhanced Accuracy and Consistency

Human error and inconsistencies are constant battles in manual backlog creation. AD goes beyond simple automation. It can be customized to analyze past project data and industry trends to generate highly accurate and consistent backlogs for your organization. This minimizes rework and delays and ensures everyone works from the same roadmap.

Improving Backlogs with User Stories and Templates

Effective backlogs rely heavily on user stories—brief descriptions of features or requirements from the end-user's perspective. Accelerated Discovery takes user story creation and management to the next level. Let's find out how:

• AI-powered user story generation: AD analyzes project requirements, identifies potential user stories, and automatically generates them in a standardized format. This saves time, ensures consistency and clarity, and reduces misinterpretations.

• Prioritization for maximum value: AD uses AI to prioritize user stories based on business value, complexity, and dependencies. This prioritization helps teams focus on the most critical features, ensuring the project delivers maximum value to end-users.

• Pre-built backlog templates: Many AI-powered project management tools, including AD, offer the ability to leverage backlog templates. These templates provide a structured framework for organizing and prioritizing essential tasks, user stories, and other backlog items, ensuring adherence to best practices. Insurance delivery teams can save time and effort by customizing AD to use their organization’s templates, which act as a pre-defined roadmap for the project. This allows them to focus their energy on the analytical process of reviewing the AD backlog result vs conforming the result into the proper format prior to JIRA integration.

The Future of AI-Powered Backlog Generation

The insurance industry is constantly evolving, and AI-powered backlog generation is poised to play a pivotal role in the future of project management. As AI technology advances and becomes more accessible, we can expect to see even more sophisticated features and functionalities. These advancements will likely include:

1. Enhanced Natural Language Processing (NLP)

AI will become even better at understanding natural language, enabling more intuitive and user-friendly backlog creation. Teams can describe their project requirements in plain English, and the AI will automatically generate a comprehensive product backlog.

2. Machine Learning-Driven Prioritization

Machine learning algorithms will become adept at analyzing vast amounts of data to prioritize backlog items based on a broader range of factors, such as market trends, customer sentiment, and regulatory requirements. This will allow insurance engineering teams to focus on tasks impacting the project's success.

3. Real-Time Backlog Updates

AI will facilitate real-time updates to product backlogs. As project requirements evolve and change, the backlog will automatically adjust to reflect these modifications. This ensures all stakeholders access the most up-to-date information, leading to better decision-making and improved project outcomes.

Conclusion

AI-powered product backlog generation presents a compelling opportunity for insurance development teams to streamline project management and solidify a competitive edge. By leveraging the power of AI and our proven set of prompts with any open source LLM, organizations can unlock many benefits: significantly reduced time and effort, enhanced accuracy and consistency in backlogs, improved team collaboration through better communication, and substantial cost savings.

While implementing AI-driven solutions like Accelerated Discovery may require an upfront investment, the long-term gains in efficiency, productivity, and cost optimization far outweigh the initial cost. As AI technology evolves, insurance development teams that embrace this innovative approach to product backlog generation will be well-positioned to thrive in the digital age.