Home Insurance e-Marketplace
Innovative Methodology & Insurtech (as subset of fintech):
Artificial Intelligence & Machine Learning driven e-insurance marketplace/ interface that identifies term insurance underwriting for home loans as a major tool that protects borrower’s interests while saving an asset before moving to the default queue.
Market Analysis and Introduction:
- Bank Ratios: Nonperforming loans to total gross loans measures bank health and efficiency.
- A high ratio may signal a deterioration of the credit portfolio and asset quality. NPA IMF Loans Definition - when payments of principal and interest are 90 days or more past due or when future payments are not expected to be received in full. (Statistical concept and methodology)
Problem Statement Challenge: Banks have written off Rs 11.17 lakh crore bad loans from their books in the last six years till the financial year 2021-22, Dec 20, 2022, The Economic Times.
Development Relevance & Problem Size: The size and mobility of international capital flows make it increasingly important to monitor the strength of financial systems. Robust financial systems can increase economic activity and welfare, but instability can disrupt financial activity and impose widespread costs on the economy. As per RBI Gross NPAs Information about India's Non-Performing Loans Ratio is 7.8 % as of December 2022. As per Eureka Hedge, 4.3 trillion $ is the global addressable market size for distressed assets. The regulatory framework for stressed assets resolution is favourable. The strong demand for capital from banks and corporates. The long-term capital with expertise — the need of the hour
Problem - What is the problem?
A salaried customer's journey with a home loan product is for 25 – 30 Years. LTV is capped at 80-95% as per prime and subprime history on credit reports. This is a long journey for any individual associated with a product home loan. Market Unmet Need Analysis: Any income sustainability issues, unfortunate events (life insurance coverage is available) like Job Loss, Epidemic (Covid-19), Job Switch – (Low to High Salary & High to Low Salary). Example - Assuming the loan defaults at the 7th Year with an EMI balance of 13 Years – this insurance will adjust the necessary cost till EMI is readjusted again.
Our short analysis of insurtech market entry strategy in China:
- Going digital will help an insurance company develop and remain competitive, even with traditional insurance products.
- The insurtech ecosystem in China consists of traditional and online insurers, internet and tech companies, and start-ups.
- In past Insurtech has gone through two stages of development in China. The first, begun in 2001, focused on online distribution (ie, selling traditional insurance products through online portals or mobile channels) by major traditional insurers like CPIC, Taikang, and PICC.
Innovation Point: The innovation point of this solution is to integrate term insurance assured amount as a measure to tackle the default situation. We can use the predictive analytics model to estimate the likelihood of default and determine the appropriate term insurance assured amount for each customer. This would act as a safety net for both the lender and the borrower, reducing the risk of default and ensuring that the loan is repaid in case of unexpected events.
Comparison with Existing Solutions: Currently, most lenders use traditional credit scoring models to assess a borrower's creditworthiness. However, these models are limited in their ability to predict default since they rely primarily on credit history and consider into account other factors that could impact a borrower's ability to repay the loan.
Our solution, on the other hand, uses advanced machine learning algorithms that can identify patterns in customer behaviour and predict the likelihood of default more accurately. Additionally, the integration of term insurance assured amount is a unique feature that is not currently offered by existing solutions. Credit-based insurance score models used by insurers are designed to predict the risk of loss. Insurers use credit-based insurance scores for underwriting to assign consumers to a pool based on risk and then for rating by deciding how to adjust the premium up or down. This Insurtech is converting credit risk management into a new form like many other components in insurance business. Leveraging real-time data allows for enhanced underwriting models and optimized pricing decisions. The microservices architecture can be built using containerization technologies like Docker and container orchestration platforms like Kubernetes. Overall, using AI/ML and microservices architecture, we can build a robust, scalable, and cost-effective solution for reducing the defaulter rate in home loans.
The roadmap for building this solution would involve following steps:
- Data Collection: Gather data on customers who have taken home loans in the past, including demographic information, loan amount, loan duration, credit history, and other relevant factors.
- Data Cleaning and Preparation: Clean and pre-process the data to remove any inconsistencies or errors.
- Feature Engineering: Create new features that could potentially influence the outcome, such as debt-to-income ratio, loan-to-value ratio, and employment history. This can be done using machine learning algorithms.
- Model Selection: Choose the most appropriate machine learning algorithms that can best predict the likelihood of default. Some of the commonly used algorithms for classification tasks include logistic regression, decision trees, and random forest. This can be done using AI/ML platforms or services.
- Model Training and Validation: Train the model on the training dataset and evaluate its performance on the validation dataset. The performance of the model can be measured using metrics such as accuracy, precision, recall, and F1 score. This step can be automated using AI/ML platforms or services.
- Deploying the Model: Deploy the model in a microservices architecture and integrate it with the home loan application process.
- Monitor and Refine: Continuously monitor the model's performance and refine it as necessary to improve its accuracy and effectiveness.
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Why-Data Convergence To Product : Home Loans Purchase Scenario
Scenario for developing this product
A) Housing loans contribute around 50% of personal/retail loans.
B) Housing affordability has improved over the last eight years.
C) With affordability rising, one can buy a bigger property with their current income.
D) Home loan rates are down by about 1.2-1.3% compared to last year alone.
Market Sentiments in Past
1. COVID-19 pandemic outbreak and lock down - the home mortgage finance market in India was heavily impacted.
2. Limited job prospects and a drop in income; some people even lost their jobs during the pandemic, which resulted in a fall in the housing market and housing loan demand.
Our journey has always been like a magnetic compass where clients are directions.
Gaurav Santoshwar is a diverse experienced analytical and fast-paced with proven skills in presentation, team, decision making, and problem-solving developed projects for large corporations in a multicultural environment.
His skill sets are Business & Corporate Strategy, Corporate and Strategic Planning, Certified Financial Modeler, Mergers and Acquisitions Prospects Analysis, Business Process Analysis and Modelling, Certified Six Sigma Green Belt, BFSI Operations, Competitor Analysis. He consulted, interacted, and solved complex business issues with clients from Germany, Netherlands; Belgium (Europe), Singapore, the United Kingdom, Gulf Countries, and the USA. He stayed and worked in European countries like Germany and Belgium in the past. During his past experience, he gained skills in Operational Management, Data Analysis Management, and Process Improvement. His strengths lie in identifying and analyzing key issues based on thorough research, improving processes, and developing business strategy in line with the latest operational requirements and trends. Prior to this, he worked with as Regional Director with Red Phoenix Agency to spearhead the challenge of setting up their operations in India based on an understanding of the market potential of several business verticals in the marketing communications space. He was also associated with Dow Corning as Strategy Analyst for their specialty silicone chemical business and Major Market Manager for their Leather Chemicals, Agrochemicals in Performance Industry Practices. As Manager for Corporate Planning and Knowledge Management where he developed an innovative framework on Microsoft Sharepoint. His internships in Bertelsmann were on Corporate Development and Strategy for Indian Media Market Analysis organic growth and merger and acquisition options and SAP as Management Consulting Intern on operational cost savings. He also had pre-MBA experience with ICICI Bank in their International Banking Group and Asian PPG Industries Limited.
Rohit Gathibandhe comes with 17 years of professional experience along with international exposure to retail and commercial lending and banking systems based in Europe and Australia, specialized in delivery of customer centric product using Agile methodology. Known for successful relationship development, collaboration enablement as well as building synergies across Software Engineering, Infrastructure, Business Owners, and Stakeholders. Highly successful in bringing product vision to life. Adept at identifying opportunities to implement new features to drive customer success.
- Help gather, synthesize, or use relevant data to inform the design of insurance products tailored to populations at greater risk of facing shocks such as climate disasters, health-related shocks, and unstable markets
- India
- Prototype: A venture or organization building and testing its product, service, or business model, but which is not yet serving anyone
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The breakup of this problem statement in 2 parts
- Feasibility of Home Loan vis a vis term/any Insurance as a solution over the spread of 25-30 years against job losses or unfortunate events currently this solution is for 3 months as rider to home insurance and life insurance to cover debt
- Followed by insurtech model like e-insurance marketplace.
Our proposed solution is for securing home loans against term insurance with a 3 Month Job Loss rider scenario in India. We understand that the market demands innovative ideas that can address critical issues faced by customers.
We believe that certain components of our proposed solution already exist in the form of existing insurance marketplaces in India. These marketplaces can potentially serve as a customer delivery mechanism for our proposed solution. We are in discussions with financial institutions like HSBC and ICICI to leverage their underwriters, to assess the risks, and design policy premium plans accordingly. This collaboration can provide us with the solid foundation we need to address potential concerns about the feasibility and effectiveness of our proposed solution.
Our proposed solution has the potential to create a new market segment that can benefit both the insurance industry and the mortgage market. We understand the importance of addressing the challenges faced by salaried individuals in repaying their EMIs after losing their jobs, and we are committed to developing a working prototype that can effectively address these challenges.
We believe that our proposed solution can disrupt and transform the market by addressing a critical issue faced by salaried individuals in India. We are confident that our proposed solution can create a significant impact in the market and create a new market segment.
As a child reference of "home insurance" from e- marketplaces like policy bazaar, insurance Dekho, Cover fox, etc. in India this serves mostly salaried individuals with credit scores.
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We already are in process to talk with other financial institutions, in past we spoke with HSBC, ICICI Bank, G A Capital, Insurance Regulatory Development Authority of India(IRDAI) etc. feedback was realignment with general insurance companies and insurance underwriters.
We built several building blocks through exhibited strengths and professional experiences.
Strength # 1 - Innovation & Anticipation - We understood the basics of business processes associated with servicing mortgage banking retail lending practices for US customers. We devised a paperless process at ICICI Bank speculated an impact through digitization and future projections on market indicators that radically impacted the process workflows using LEAN principles methodology tools and techniques like KANBAN, POKA YOKE, MUDA, KAIZEN, 5S. It was later nominated for patenting. (FINTECH).
Recently, Gaurav is also involved Atlanta, Georgia based Arab Investment Fund Management on fund raising low interest high value base collateralized debt loan and was involved in the real-life fundamental problem-solving activities with venture capital and a private equity organization named Faster Capital.
Strength # 2 - Strategic Thinking & Presentation: - Both of us have experiences to create impactful presentations aligned to top strategy outlook.
Strength # 3 - Entrepreneurship and Scratch Technology Modelling - Working in past with several banking and financial institutions undertook projects on e-business initiatives on creating the strategic framework and best practices implementations.
Strength # 4 - Analytical - We worked on client side and back end with business planning and analysis work scanned and applied for bids on highly complex projects with banking and financial institutions.
Strength # 5 - Problem Solving: With proven ability to work on high-value international projects with Multinational Corporations, C-Level Executives, and Board of Directors in the past, applying to Solve will help us to dwell tirelessly on this particular problem making us more agile, impactful and adaptive towards addressing this global people problem dealing with three basic pillars of life like Food, Clothing and Shelter. This would help acquire visibility, versatility and competence needed to reach full potential as an entrepreneur. It would act as launch pad to the career crafted for people and organizations. Currently, we are involved to create an impactful congruence while developing genuine products and services that will add remarkable & meaningful value to human beings and overall society. At Solve, it will be a tremendous opportunity to gain experience, experiment, react, interact, create & co-create from diverse peers that propels professional long-term partnerships.
- Financial (e.g. accounting practices, pitching to investors)
- Product / Service Distribution (e.g. delivery, logistics, expanding client base)
- Public Relations (e.g. branding/marketing strategy, social and global media)
- Technology (e.g. software or hardware, web development/design)
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A salaried customers journey with a home loan product is for 25 – 30 Years. LTV is capped 80-95% as per prime and subprime history on credit reports. This is long journey for any individual associated with product home loan.
Market Unmet Need Analysis: Any income sustainability issues, unfortunate events (life insurance coverage is available) like Job Loss, Epidemic (Covid-19), Job Switch – (Low to High Salary & High to Low Salary).
Differentiation - How is the start-up different than its competitors?
1. One click service for purchase decision
2. Organization and reorganization of data as per requirement
3. Scorecards, peripheral applications, and dashboards for easy understanding
4. Unique mathematical blends, structured thinking to draw perspectives.
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Technology Introduction
AI/ML technology has been introduced in insurtech to collect and interpret consumer data and determine risk more accurately compared to manual data interpretation. AI, ML, and automation will shape the future of insurtech. The benefits and necessity of automation are clear, and the future for the industry is rooted in technology. ML based solutions can add value to insurers – irrespective of the mode of delivery – delivered as a standalone model (standalone AI), or delivered as a part of a service or product (embedded AI). Insurers need to start looking more holistically at the way they use AI and ML. With a huge amount of data, ML can unlock opportunities for improved propositions, underwriting decisions, claims decisions, and greater customer insights.
Partnership
AI/ML are being used in insurtech to create new insurance model and customer experiences using predictive analytics.
Industry License & Department of Promotion Industrial and Internal Trade Certification (DPIIT) The DPIIT is a central government department under the Ministry of Commerce and Industry in India. DPIIT gives number of Government to Business (G2B) services such as filing of Industrial Entrepreneurs Memorandum (IEM) and Industrial License (IL) applications Online. To access tax benefits and easier compliance, to be recognized as Startups by the Department for Promotion of Industry and Internal Trade (DPIIT) under the Startup India initiative. It takes 2 months to process this request.
Executive summary
This Insurtech is the use of technology innovations designed to make the current insurance model more efficient. By using technology such as data analysis, AI/ML, insurtech allows products to be priced more competitively. Insurtech is used to process claims more effectively, evaluate risk, process contracts, or underwrite policies.
Mission Statement
This Insurtech is the combination of insurance and technology solutions for enrollment, insurance claims processing, underwriting, policy administration, data insights, fraud detection and more. This Insurtech is disrupting the insurance industry by providing cost saving solutions and better customer experience for insurance companies.
Business's Goals
This Insurtech should be new technology introduced to insurers’ operations to save on costs or streamline processes. Since this Insurtech is new, its applications are constantly evolving. It streamlines and enhances back-end processes, improves the customer experience, and saves the insurance company money.
Market Analysis
The global insurtech market size was valued at USD 5.45 billion in 2022 and is expected to expand at a compound annual growth rate (CAGR) of 52.7% from 2023 to 2030.
Marketing Strategy
The insurance industry has gone through a major revolution due to the buzz created by the intervention of technologies. To showcase your digitally transformed business, we must start marketing. Here are some marketing strategies to consider:
1. Website – A Powerful Marketing Tool: The Customer Behavior and Loyalty in Insurance report by Bain & Company found that more than half of all insurance aspirants choose an insurer only after thorough research on digital platforms. Therefore, having a website that is easy to navigate and provides all the necessary information is crucial.
2. Build Thought Leadership through Content Marketing: Content marketing is one of the powerful digital marketing strategies that can help this insurtech businesses build thought leadership and establish themselves as experts in their field.
3.Be Active on social media: Social media platforms like Facebook, Twitter, LinkedIn, and Instagram are great places to connect with target audience and build brand awareness.
4. Leverage the Perks of PPC Advertising: Pay-per-click (PPC) advertising is a great way to drive traffic to insurtech website and generate leads.
- 3. Good Health and Well-being
- 8. Decent Work and Economic Growth
- 11. Sustainable Cities and Communities
- 15. Life on Land
BRIndiaChinaS are two most populous nations believing in philosophy of affordable food, clothing and shelter, 庇护所 (Chinese), मकान (Hindi).
This product is themed around shelter amidst job losses or unfortunate circumstances.
Developed 3E core knowledge management leadership building blocks and framework using the following methodology.
Existential ^ Philosophy # Themed as “Questions and Reads,” more questions you ask more you read and vice versa.
Experimental ^ Absolute # Asymmetric Information consumed and disseminated to reach absolute leadership potential.
Experiential ^ Compute # Learnings and realizations that makes logical sense towards most equitable capabilities.
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Artificial Intelligence & Machine Learning driven e-insurance marketplace/ interface that identifies term insurance underwriting for home loans as a major tool that protects borrower’s interests while saving an asset before moving to the default queue.
3 products based on user buying ability.
Term Insurance Premium provides additional housing finance flexibility for lenders and consumers by expanding the “underwriting envelope” usually along the loan-to-value dimension.
Differentiation - How is the start-up different than its competitors?
1. One click service for purchase decision
2. Organization and reorganization of data as per requirement
3. Scorecards, peripheral applications, and dashboards for easy understanding
4. Unique mathematical blends, structured thinking to draw perspectives.
Please refer below section of the financial thesis model for clarity on revenues and profits. Term Insurance Premium provides additional housing finance flexibility for lenders and consumers by expanding the “underwriting envelope” usually along the loan-to-value dimension.
Product 1 Repayment in 1 Instalment 10000 INR/Unit
Product 2 Repayment in 5 Instalments 11000 INR/Unit
Product 3 Repayments in 10/15/20 Instalments 13000 INR/Unit. Term Insurance (Indirect and Extended Solution): Home Protection against the surcharge premium for term insurance on EMI to safeguard the interest of both borrower, financial institution, and mutual and venture funds.
Artificial Intelligence & Machine Learning driven e-insurance marketplace/ interface that identifies term insurance underwriting for home loans as a major tool that protects borrower’s interests while saving an asset before moving to the default queue.
The roadmap for building this solution would involve following steps:
AI/ML and microservices architecture
- Data Collection: Gather data on customers who have taken home loans in the past, including demographic information, loan amount, loan duration, credit history, and other relevant factors. This data can be stored in a database.
- Data Cleaning and Preparation: Clean and pre-process the data to remove any inconsistencies or errors. This step can be automated using data cleaning tools or services.
- Feature Engineering: Create new features that could potentially influence the outcome, such as debt-to-income ratio, loan-to-value ratio, and employment history. This can be done using machine learning algorithms.
- Model Selection: Choose the most appropriate machine learning algorithms that can best predict the likelihood of default. Some of the commonly used algorithms for classification tasks include logistic regression, decision trees, and random forest. This can be done using AI/ML platforms or services.
- Model Training and Validation: Train the model on the training dataset and evaluate its performance on the validation dataset. The performance of the model can be measured using metrics such as accuracy, precision, recall, and F1 score. This step can be automated using AI/ML platforms or services.
- Deploying the Model: Deploy the model in a microservices architecture and integrate it with the home loan application process. The model can be hosted in a containerized environment, and the microservices architecture can be used to manage the model's lifecycle.
- Monitor and Refine: Continuously monitor the model's performance and refine it as necessary to improve its accuracy and effectiveness. This can be done using monitoring and logging services, and the model can be updated or retrained as needed.
- A new business model or process that relies on technology to be successful
- Ancestral Technology & Practices
- Artificial Intelligence / Machine Learning
- Software and Mobile Applications
- India
- China
- United States
- Not registered as any organization
Too early to predict DEI framework
Business Model
The majority of insurtechs will fit into one of these four business model categories: Enterprise SaaS, Intermediary, Marketplace, and Enterprise.
However, this model is the Marketplace model is where an insurtech creates a platform for customers to compare and purchase insurance policies from multiple insurers. We are also under testing and experimental researching the Enterprise model is where an enterprise company sells services or software to other businesses (e.g. insurer) on a single-license basis.
Value Capturing Method
This Insurtech solutions perform a wide range of purchasing tasks, such as helping businesses compare offerings, or providing a quote and binding coverage with just a few clicks and pieces of data. These solutions are powered by strong analytics backed by big data. As this InsurTech enhances business operations and reduce costs, valuations will become more important. There are three common approaches to determining business value: asset approach, income approach, and market approach.
Sales Strategy
This Insurtech aim to create customer experiences that rival the best of retail. This could mean focusing on value (like Warby Parker), personalization (like Netflix), or efficiency (like Amazon).
Embedded services enable insurtech to form numerous partnerships with a massive spectrum of business customers who wish to offer cover products at the point of sale. Automated and digitized offerings have been turned into a fully automated workflow, thus lowering unit, and enabling insurtechs cost to scale at pace.
GTM Approach
A go-to-market (GTM) strategy is a comprehensive action plan that outlines the current approach and steps to attract and win new customers, enter new markets, increase market share, and achieve projected sales and marketing goals, revenue, and ROI. For this insurtech to scale in 2023, we are planning to focus on delivering faster, better products and services. Insurtechs should identify suitable partners within the ecosystem. We need to plan around following KPIs.
Our insurtechs KPIs are quantitative values used to determine how efficiently and effectively specific insurance operational goals and objectives are achieved over a certain period.
Some of the key performance indicators for insurtechs include
Quote Rate,
Customer Acquisition Cost (CAC),
Customer Lifetime Value (CLV),
Net Promoter Score (NPS),
Claims Ratio,
Loss Ratio,
Combined Ratio, and
Expense Ratio.
KYC in this insurtech is essential.
- Individual consumers or stakeholders (B2C)
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Economic Value Added (EVA) is a one of our important financial performance metrics that measures the residual income generated by a company after accounting for the cost of capital. Our Insurtech company can use EVA to measure financial performance and determine if they are creating value for our shareholders.
This Insurtech companies can create value by introducing new products or services (mutual and venture funds, private equities), entering new segments, and lowering acquisition costs by providing customers with a digital interface.
Currently, the company creates shareholder value by generating returns in excess of its cost of capital. It distinguishes itself from traditional financial performance metrics, such as net profit and earnings per share (EPS). We are taking a conservative approach of selling 500 units and analyze the performance in markets. With an increase of approximately 30% over 3 years for profits, the ask is meant 200000 $ with equity dilution of 3% for entering Chinese markets. We are also applying actively for grants in India and China.
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Founder/Entrepreneur
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