Each lender relationship is assigned a loan status based on transaction analysis.

How to Interpret

• Active Loan → Treat as current debt.

• Likely Settled / Fully Paid / Multiple Loans Possible → Do not treat as active credit unless further evidence suggests otherwise.

  • The duration used to classify a loan as "Likely Settled" is 90 days of inactivity. Specifically, the criteria are: The borrower has repaid ≥ 70% of the borrowed amount, and there has been no activity with that lender for the last 90 days.

• Limited Data → Ask the borrower for clarification.

Important: If the report only shows repayments but no loan disbursement, the client may be repaying a loan on behalf of someone else, or the loan may have been taken outside the statement period. In such cases, request additional information from the borrower.

2. Cashflow Score

The Cashflow Score estimates a borrower's credit risk using transaction behaviour from bank or mobile money statements.

The score reflects patterns such as:

• Income stability

• Spending behaviour

• Credit usage

• Cashflow consistency

• Financial discipline signals

The score does not rely on credit bureau data and is calculated solely from the statement provided.

3. Risk Levels

Higher scores indicate stronger financial behaviour and lower default risk.

4. How Lenders Should Use the Score

Recommended review flow:

1. Check the risk tier for overall borrower risk.

2. Review Active Loans to understand current obligations.

3. Ignore Fully Paid, Likely Settled, or Multiple Loans Possible statuses when calculating debt burden.

4. Review highlighted factors to understand key strengths or risks in the borrower profile.

The score should support credit decisions, not replace lender judgment.

5. Important Notes

• The score reflects only the uploaded statement period.

• Results may change when new transactions are analysed.

• If income or repayments occur in another account not included in the statement, they may not appear in the analysis.

The Cashflow Score is an AI-powered default prediction score delivered through the CashIQ platform by Cladfy Inc. It is not a credit bureau score; it does not access CRB data, credit reports, or traditional credit histories.

Instead, it analyzes only the transaction data available, M-PESA statements, bank statements, and other mobile money data (e.g., MTN MoMo, Airtel Money) to predict the likelihood that a borrower will default on a loan. The score ranges from 300 to 850, where higher scores indicate lower default risk.

The engine uses a proprietary multi-pillar analysis framework adapted specifically for cashflow patterns across African mobile money and banking markets. The accuracy of the score is directly tied to the volume and quality of transaction data provided.

Five Credit Pillars

Every score is built from five independently weighted pillars that assess default risk from different angles:

The engine also applies cross-pillar interaction adjustments; consistent strength is rewarded, while critical weakness in any pillar triggers compounding penalties. For example, low income combined with heavy debt is penalized more harshly than either alone.

What Data Goes In

The engine consumes all available financial data as a combined input: M-PESA statements, bank statements, and other mobile money platforms (e.g., MTN MoMo, Airtel Money) when available. The more data sources provided, the more accurate and comprehensive the score.

Beyond raw transaction data, the CashIQ platform extracts deeper financial signals that are invisible in transaction records alone:

Every enriched field is optional. The engine uses whatever is available and falls back gracefully. Even a single enriched field improves accuracy.

Risk Tiers

Built-in Safeguards

Explainable Scoring : top_factors

Every response includes helping and hurting factors that explain the score in plain language:

"top_factors": {
  "helping": [
    {"signal": "monthly_income", "label": "Average monthly income", "value": "XX.XX"},
    {"signal": "savings_behavior", "label": "Savings activity", "value": "XX.XX"},
    {"signal": "exact_repayment_rate", "label": "Verified loan repayment rate", "value": "XX.XX"}
  ],
  "hurting": [
    {"signal": "gambling_penalty", "label": "Gambling activity risk", "value": "XX.XX"},
    {"signal": "zero_balance_rate", "label": "Zero-balance frequency", "value": "XX.XX"},
    {"signal": "spending_rate", "label": "Spending rate vs income", "value": "XX.XX"}
  ]
}

• value: 0.0 (worst) to 1.0 (best) for each signal

• Helping: the applicant's strongest credit signals

• Hurting: where they're weakest; and what they'd need to improve

Use this for:

• Underwriter review: quick insight into why a borderline applicant scored where they did

• Applicant feedback: actionable guidance ("reducing your overdraft usage would improve your score")

• Audit trail: full signal-level transparency

API Integration

Example

API Request

{
  "overview": {
    "mpesa_transaction_count": "XX.XX",
    "paid_in_average_pm": "XX.XX",
    "loan_dependency_ratio": "XX.XX"
  },
  "data": {
    "summary": {
      "loans_taken_count": "XX.XX",
      "loans_repaid_count": "XX.XX",
      "zero_balance_rate_percentage": "XX.XX"
    },
    "cashflow": {
      "monthly": [
        {"month": "Period", "total_in": "XX.XX", "total_out": "XX.XX"},
        {"month": "Period", "total_in": "XX.XX", "total_out": "XX.XX"}
      ],
      "averages": {"avg_monthly_in": "XX.XX", "avg_monthly_out": "XX.XX"}
    },
    "savings": {"details": {"total_amount": "XX.XX", "total_transactions": "XX.XX"}},
    "gambling": {"overview": {"total_amount": "XX.XX", "total_transactions": "XX.XX"}},
    "disposable_income": {
      "last_3months_disposable_income": [
        {"month": "Period", "amount": "XX.XX"},
        {"month": "Period", "amount": "XX.XX"},
        {"month": "Period", "amount": "XX.XX"}
      ]
    }
  }
}

API Response

{
  "request_id": "a1b2c3d4-...",
  "prediction": "XX.XX",
  "probability": "XX.XX",
  "score": "XX.XX",
  "risk_tier": {
    "tier": "Tier",
    "min_score": "XX.XX"
  },
  "top_factors": { "helping": [...], "hurting": [...] },
  "pillars": {
    "income_capacity":          {"label": "Income & Capacity",          "score": "XX.XX"},
    "cashflow_stability":       {"label": "Cash Flow Stability",        "score": "XX.XX"},
    "debt_leverage":            {"label": "Debt Burden & Leverage",     "score": "XX.XX"},
    "repayment_behavior":       {"label": "Repayment Behavior",         "score": "XX.XX"},
    "financial_sophistication": {"label": "Financial Sophistication",   "score": "XX.XX"}
  },
  "data_sufficient": "true/False",
  "enriched_data_available": "true/False",
  "scoring_version": "XX.XX",
  "latency_ms": "XX.XX"
}

How the Score Is Produced

The engine uses a proprietary two-stage architecture:

1. Machine Learning Model: A trained model processes the available transaction data to produce a default probability.

2. Multi-Pillar Analysis: The probability is combined with proprietary signal analysis across all five pillars, cross-pillar interactions, and calibration curves to produce the final score.

Design principles:

• ✅ Transaction-data only, no CRB, no credit bureau, no external data beyond provided statements

• ✅ Multi-source, M-PESA, bank statements, and other mobile money data combined for maximum accuracy

• ✅ Data-limited, the score only reflects what the available transaction data supports

• ✅ Deduplicated, overlapping data sources are automatically resolved to prevent double-counting

• ✅ Deterministic, same input always produces the same score

• ✅ Calibrated, score distribution maps to real-world default rates

• ✅ Transparent, full signal-level explainability for every decision

Frequently Asked Questions

1. How should I use top_factors? Helping factors explain why a score is high; hurting factors explain why it isn't higher. Share with applicants as actionable feedback or use for underwriter review of borderline cases.

2. Is the score deterministic? Yes. Identical input always produces an identical score. No randomness.

3. How does deduplication work? When multiple data sources contain overlapping information, the engine automatically uses the most accurate source and disregards the duplicate. This prevents unfair double-counting.

4. What about applicants with a very short history? The engine applies a thin-file score cap to applicants with limited transaction history, who can only achieve a limited maximum score until more data becomes available.

5. How does gambling affect the score? Proportionally. Occasional small amounts have minimal impact. Gambling exceeding a significant portion of income triggers a meaningful penalty that affects the overall score, not just one pillar.

6. How often should I re-score a borrower? Re-score whenever you have a new or updated statement. For existing borrowers, a quarterly re-score using the latest 6-month statement provides good monitoring coverage.

If you’re a lender offering short-term loans, you’ve probably dealt with Flat Interest Loans that run for just a few weeks.
Let’s say you want to create a loan product that charges a 25% flat interest rate for 6 weeks; how do you configure it correctly for your client?

Here’s a quick guide 👇

Step 1: Convert 6 Weeks into a Year Fraction

There are 52 weeks in a year.
To find out how many 6-week periods fit into one year:

Step 2: Calculate the Simple Annualized Rate

If the loan is not compounded (i.e., interest isn’t charged on previous interest), we can scale it linearly:

See the two Examples Below

Step 3: Configure It in Cladfy Microlender

Add a new Loan Product in your Cladfy Microlender dashboard with the sample format below:

Once you save the product, Microlender automatically applies the correct interest calculations, repayment schedule, and reporting metrics.

Why This Matters

This understanding is critical for:

• Lenders, to maintain transparency and accurate reporting.

• Borrowers, to make informed financial decisions.

🚀 Ready to Streamline Your Lending Operations?

Manage your microfinance business on one system with Cladfy Microlender - your end-to-end lending management system

👉 Sign up today at cladfy.app and start lending with confidence using a fast, high-quality, cloud-based software designed for microfinance and digital lenders.

Bring Order to the Chaos

That’s where Logiclabs comes in. We built this platform specifically for lenders like you – the ones who know their borrowers by name, who understand the local economy because you live in it, and who deserve powerful, tailored tools to grow and succeed.

You Handle Relationships, We Handle the Tech

Here’s our philosophy: you focus on what you do best – building relationships, disbursing loans, and collecting payments. Meanwhile, we handle the technology that makes your decision-making smarter, faster, and more consistent — plus other parts of the loan lifecycle management, of course :).

The beauty of Logiclabs is that it’s built for real-world lending. We know your borrowers don’t fit into neat little boxes. Some have informal income, limited credit history, or unique circumstances that generic scoring models simply miss.

Customize Without the Complexity

With Logiclabs, you can:

• Use insights from your historical data to get started

• Build scorecards that reflect your actual lending criteria

• Adjust models based on your local market conditions

• Deploy changes quickly without needing a data science degree

• Make consistent decisions across your team

• Track performance and refine your approach over time

A credit scorecard that works beautifully in urban areas might need tweaking for rural borrowers. A model that fits small business lending may not be right for agriculture. Logiclabs gives you the flexibility to account for those differences — without the complexity.

Real Tools for Real Lenders

We created Logiclabs after seeing how small and medium lenders often struggle with tools that don’t reflect their reality. Traditional systems can be too expensive, too rigid, or just not the right fit.

This creates missed opportunities — for both lenders and borrowers.

Logiclabs bridges that gap with scoring capabilities that are sophisticated yet accessible — without months of setup or an expensive tech team.

Let’s Talk

If you're a microfinance lender ready to bring more precision to your lending — without losing your human touch — we should chat. Logiclabs might be exactly what you've been looking for.

That’s it. That’s the blog.

…Okay, not quite. But this one-liner captures a fundamental truth that too many overlook in the rush to build sophisticated credit models.

Why Start Simple?

When we talk about cash flow underwriting—especially in the age of alternative data and AI—it’s easy to default to complexity. Buzzwords like machine learning, behavioral risk scoring, and synthetic variables dominate the conversation.

But at the core of effective underwriting lies one thing: intuitive signals that make sense. Not just to data scientists and engineers, but to credit analysts, execs, and even the borrowers themselves.

Attributes like:

• Number of days with a negative balance – A straightforward proxy for financial strain.

• Current balance – A snapshot of liquidity at any point in time.

• Time since highest or lowest balance – Indicates stability, volatility, and financial direction.

• Monthly net inflow volatility – Tells you how predictable someone’s income is.

• Average end-of-month balance – Reflects long-term cash discipline and surplus capacity.

Each of these is defensible. Explainable. Operationally sound.

From Intuition to Infrastructure

You don’t need a PhD to make these work. You need clean cash flow data, a clear objective, and a willingness to stay grounded in reality. With just 3 to 5 of these intuitive metrics, you can build an underwriting model that:

✅ Predicts default risk effectively
✅ Holds up in credit committee discussions
✅ Aligns with real-life financial behavior
✅ Lays the foundation for more advanced modeling later

And once you’ve established trust and consistency with these basics, the doors open to more sophisticated features—transaction classification, seasonality modeling, merchant-specific behavior, etc.

Don’t Overthink It. Just Start.

Cash flow underwriting is not a mystery. It’s just underutilized. And part of the reason is that people think it has to be complicated to be credible.

It doesn’t.

In fact, the more intuitive your starting point, the more scalable your approach becomes. You build buy-in. You build understanding. And ultimately—you build better credit outcomes.

👉 Want to see how we help lenders do this at scale?
Visit cladfy.com/cashiq to learn more about how we turn cash flow data into actionable credit intelligence.

At Cladfy, we continuously enhance our fraud detection capabilities to help lenders and businesses verify financial statements with confidence. Our latest feature detects signs of tampering and fraud in bank and mobile money statements, providing transparency in the credit evaluation process.

How We Detect Fraudulent Activity

Cladfy’s CashIQ uses advanced AI-driven analytics to assess financial statements submitted during credit assessments and KYC/KYB workflows. Our system scans each statement for inconsistencies and manipulation, flagging any suspicious activity. While our fraud detection system assigns an Authenticity Score, we strongly recommend reviewing all statements, regardless of their score, to ensure accurate and informed decision-making.

Authenticity Score: (XX/100)

Based on multiple fraud signals, our system categorizes statements as highly suspicious, moderately suspicious, or fairly suspicious. We generate detailed reason codes to provide transparency on why a statement may not be authentic.

Detected Tampering Signals:

• PDF Editing Software Detected: Our system identifies whether a statement was altered using software commonly used for modifying PDFs, raising concerns about its legitimacy.

• Account Holder Name Modification: Any changes to the original account holder’s name indicate a potential case of identity fraud.

• Account Number Modified: We detect modifications in the account number, signaling an attempt to misrepresent financial details.

• Suspicious Statement Origin: Our analysis of metadata and statement structure helps us identify documents that may have originated from unauthorized or non-bank sources.

• Transaction Section Tampered: If transaction histories appear inconsistent or manipulated, our system flags them as a strong indicator of fraudulent activity.

Why This Matters for Lenders and Businesses

Fraudulent financial statements can lead to poor lending decisions, increased defaults, and financial losses. Detecting tampering early helps:

• Reduce the risk of lending to unqualified borrowers.

• Prevent identity fraud and financial misrepresentation.

• Strengthen regulatory compliance in KYC/KYB processes.

• Improve operational efficiency by reducing manual fraud investigations.

What’s Next?

Cladfy remains committed to helping financial institutions combat fraud through AI-driven insights and automated fraud detection. Our tampering and authenticity checks provide lenders with an added layer of security, ensuring that every statement is thoroughly analyzed before making critical financial decisions.

We go beyond just detection. Our platform integrates these fraud signals into your lending workflows, enabling seamless fraud assessment and risk mitigation. By leveraging machine learning and pattern recognition, we provide lenders with a deeper understanding of statement integrity, reducing fraud-related risks while ensuring legitimate borrowers receive timely financial services.

Interested in learning how our fraud detection technology can enhance your credit assessment workflows? Contact us today or visit Cladfy to schedule a demo.

Its user-centric design allows lenders to focus on strategic priorities rather than getting bogged down in administrative tasks, thanks to its easy setup and seamless functionality. Compared to other systems, the Cladfy Loan Management System (LMS) stands out with its customisable features, scalability, and ability to streamline lending operations and enhance borrower experiences.

Here is why African Microfinance Lenders are choosing the Cladfy Loan Management System.

1. Streamlined Design

   • The Cladfy LMS offers an intuitive and user-friendly interface, simplifying the loan management process for users of all technical levels.

2. Automation and Efficiency

   • With highly automated workflows, the system reduces manual intervention, enabling users to focus on strategic tasks such as portfolio growth and borrower engagement.

3. Intelligent Analytics

   • Cladfy LMS integrates advanced analytics tools to provide insights into borrower behavior, credit scoring, and risk management, helping lenders make informed decisions.

4. Customizability

   • The platform is tailored to the unique needs of lenders, with flexible configurations for loan products, fees, and interest rate structures.

5. Performance and Scalability

   • The system is built for scalability, allowing lenders to manage high volumes of loans while maintaining optimal performance.

6. Seamless Navigation

   • The dashboard design allows users to navigate quickly between client details, loan applications, and repayment histories, ensuring a smooth operational experience.

7. Enhanced Accessibility

   • Mobile-friendly access ensures that users can manage their portfolios on the go, staying connected with borrowers wherever they are.

8. Data Security and Reliability

   • Cladfy LMS is powered by robust security protocols, ensuring that sensitive borrower and lender data is protected against breaches and unauthorized access.

9. Integrated Features

   • The platform supports integrated functionalities like automated reminders, payment gateways, and credit scoring, reducing the need for third-party tools.

10. Exceptional User Support

    • Dedicated customer support ensures that users receive assistance promptly, reinforcing a sense of reliability and trust in the platform.

11. Real-Time Updates

    • Live updates on repayments, disbursements, and borrower activities keep users informed, enabling proactive decision-making.

12. Impact on Financial Inclusion

    • Designed with a mission to bridge the credit gap, Cladfy LMS empowers microfinance lenders to reach underserved communities effectively.

13. Holistic Experience

    • The system ensures that every interaction, from loan application to repayment, is streamlined, creating a comprehensive, end-to-end loan management experience.

Talk to us and see how the our software solutions delivers value, blending technology, usability, and impact.

Here’s how one particular technique; cashflow underwriting is making a difference:

🔹 Faster Bank Statement Processing

For years, manual reviews of bank statements have been a bottleneck for microfinance operations. Analysts spend hours or even days combing through statements, checking for patterns, and verifying cashflows.

With automated tools, this process has become almost instantaneous. What used to take hours now takes mere minutes, allowing lenders to process more applications in less time. This increased efficiency also means that borrowers can receive decisions faster, enhancing their experience and satisfaction.

🔹 Zero Printing Fees

Microfinance lenders often rely on paper documents for processing and verification. Printing, storing, and managing these documents comes with significant costs—not to mention the environmental impact.

By going fully digital with cashflow underwriting, lenders eliminate the need for printing altogether. Digital tools extract and analyze financial data directly from electronic statements, reducing expenses and supporting sustainability. 🌱

🔹 Lower Customer Acquisition Costs (CAC)

Customer acquisition is a major expense for lenders. Traditional methods involve heavy marketing investments and long onboarding processes, which drive up costs.

Cashflow underwriting helps lenders target borrowers more effectively by focusing on clients with stable and verified cashflows. This targeted approach minimizes marketing waste and streamlines onboarding, resulting in significantly lower CAC. It’s a win-win: lenders save money, and borrowers benefit from a more straightforward application process.

Why Cashflow Underwriting Matters for MSMEs

Micro, small, and medium enterprises (MSMEs) are the backbone of many economies, but they often struggle to access credit due to limited financial data or collateral. Cashflow underwriting addresses this challenge by prioritizing real financial activity over traditional credit metrics.

By analyzing an MSME’s cashflow, lenders can make informed decisions about loan eligibility and repayment capacity, ensuring that deserving businesses get the funding they need to thrive. This approach not only drives financial inclusion but also fosters economic growth in underserved communities.

Ready to Transform Your Lending Operations?

Cashflow underwriting is the future of microfinance, and Cladfy is here to help you get there. Whether you’re looking to :

• Save time with fast, automated processes.

• Cut costs by going paperless.

• Lower CAC with data-driven client targeting.

…we’ve got you covered.

Let’s connect and explore how we can revolutionize your lending business. 🚀

Omnichannel Loan Management

Our platform supports loan management across multiple channels:

1. In-person: For face-to-face interactions, we offer tools for onboarding, KYC/KYB, credit assessment, loan offers, disbursement, payment schedules, repayment, and notifications.

2. Over the phone: Enable virtual onboarding, KYC/KYB, loan offers, and notifications for phone-based customer service.

3. On the web: Empower customers with self-onboarding, online KYC/KYB, credit assessment, loan offers, payment schedules, repayment, and notifications.

4. Social media/chat: Facilitate virtual onboarding and send notifications through popular messaging platforms.

Key Features

• Comprehensive onboarding: Streamline the process whether in-person or digital

• Robust KYC/KYB: Ensure regulatory compliance across all channels

• Flexible loan management: From credit assessment to repayment tracking

• Multi-channel notifications: Keep borrowers informed through their preferred medium

By unifying these processes on a single platform, Cladfy helps financial institutions:

• Improve operational efficiency

• Enhance customer experience

• Maintain consistency across channels

• Reduce manual errors

• Gain better oversight of the entire loan lifecycle

Our end-to-end solution adapts to your business model, whether you're a traditional microfinance bank, an online lender, or a fintech startup. With Cladfy, you can focus on growing your loan portfolio while we take care of the back-end complexities.

To learn how Cladfy can transform your loan management processes, contact us for a demo today.

Cladfy's Credit Assessment Suite offers a range of tools that complement each other to provide a well-rounded credit assessment:

1. Loan ability Estimate: Analyzes bank statements to assess income stability and repayment capacity.

2. Repayment Score: Evaluates historical repayment behavior to predict future performance.

3. Demographic Score: Considers borrower demographics like age, education, occupation, number of dependents, and income to assess creditworthiness for those with limited credit history.

4. Deposit Score: Analyzes deposit history to understand a borrower's savings habits and potential for meeting future financial obligations.

👍 By combining these tools, lenders gain a deeper understanding of a borrower's financial health, leading to:

• Reduced Risk: More comprehensive data allows for better risk assessment, minimizing defaults.

• Responsible Lending: Informed decisions ensure borrowers receive suitable loan products that meet their needs.

• Financial Empowerment: Fintech lenders can empower borrowers by offering them credit opportunities they may not have qualified for otherwise.

Our APIs are designed to be easy to integrate with existing loan origination systems. We also offer a comprehensive suite of tools and resources to help you get started, including a live demo and a detailed API reference.

Next steps:

1. Learn more about our credit scoring APIs

2. Contact us via info@cladfy.com.

We look forward to helping you improve your lending decisions and positively impact your borrowers' financial lives.

We believe that our credit scoring APIs can help lenders make a positive impact in the financial lives of their borrowers. We invite you to learn more about our APIs and how they can help you improve your lending decisions.

Harambeans fosters collaboration amongst Africa's brightest minds, uniting them in a shared mission to unlock the continent's vast potential. By joining forces, these visionary leaders are not only building successful businesses but also laying the groundwork for a thriving African ecosystem.

As put by Ebby in her LinkedIn post.

The week culminated in inspiring masterclasses held at Harvard Business School and Massachusetts Institute of Technology. These sessions delved into the journeys of groundbreaking founders in emerging markets, showcasing how they transformed their ventures from fledgling startups to billion-dollar enterprises. Their success stories were a testament to the power of unwavering focus on solving critical problems, exceptional execution, and a deep commitment to customer needs.

Witnessing these inspiring journeys firsthand instilled a powerful sense of belief – what once seemed like an impossible dream, then an improbable one, now feels like an inevitable reality for Africa's future.

We extend our heartfelt gratitude to Harambeans Chairman Okendo Lewis-Gayle, Denver Isaacs, and their entire team for hosting this incredible symposium. Cladfy is honored to be a part of this transformative movement, and we stand alongside our fellow Harambeans, united in building a brighter future for Africa.

#Harambeans #AfricanLeadership #entrepreneurship, #integrity, and #impact.

Microfinance institutions (MFIs) are crucial lenders to hundreds of millions of SMEs in emerging markets. However, their traditional processes and the cost of technology have often exposed them to limitations in scalability, efficiency, and personalization. Generative AI (Gen AI), a type of artificial intelligence capable of creating new content, presents a transformative opportunity for MFIs to address these challenges and unlock new possibilities.

Understanding Gen AI:

Gen AI is a type of artificial intelligence that creates new content, like text, images, or code, based on what it has learned from existing data. This technology, specifically large language models (LLMs), holds immense potential for MFIs by:

• Automating repetitive tasks: LLMs can handle tasks like generating loan applications, reports, and personalized communications, freeing up staff time for more complex activities.

• Enhancing customer service: LLMs can power chatbots that answer frequently asked questions, schedule appointments, and provide basic financial guidance, improving accessibility and responsiveness.

• Data-driven decision-making: LLMs can analyze vast amounts of data to identify patterns and trends, aiding in credit risk assessment, product development, and customer segmentation, leading to more informed decisions.

Practical considerations for MFIs:

1. Data Security:

While LLMs offer numerous advantages, data security remains paramount. Here's how Cladfy addresses this concern:

• Self-hosted models: Cladfy utilizes its own, self-hosted models, ensuring data remains within the organization's control and preventing leaks to third parties.

• Grounding the models: Cladfy employs techniques to prevent LLMs from generating inaccurate information ("hallucinating") by grounding them in factual data and real-world scenarios.

• Open-source foundation: Cladfy leverages open-source models as a foundation, further enhancing transparency and control over the data used.

2. Choosing the right use cases:

MFIs can adopt a flexible approach to Gen AI implementation, starting with low-risk, high-impact use cases like:

• Generating personalized communications: LLMs can tailor loan repayment reminders, account updates, and marketing messages to individual customer preferences.

• Automating loan application processing: LLMs can streamline the application process by extracting key information from documents and verifying data accuracy.

• Creating educational content: LLMs can generate informative articles and videos on financial literacy topics, empowering customers to make informed decisions.

3. Future potential:

Gen AI is still in its early stages, but its potential to transform the financial landscape is vast. As technology advances, we can expect:

• Ubiquitous AI assistants: Imagine a future where everyone has a personal AI assistant aiding with financial tasks, offering personalized advice, and simplifying financial management.

• Autonomous agents: These AI-powered programs could automate complex tasks like comparing loan options from different MFIs, ensuring customers get the best deals based on their needs.

Getting Started with Generative AI

Cladfy advocates for a cautious yet progressive approach to integrating generative AI:

• Start Small, Scale Up: We follow a "crawl, walk, run" strategy, beginning with low-risk applications using non-sensitive data. This allows for rapid experimentation, early wins, and gradual staff buy-in before venturing into more complex use cases.

• Focus on Impact: We prioritize applications with the potential to significantly improve efficiency, client experience, or decision-making. The table below outlines some potential use cases for MFIs:

See it in action:

We are continuously integrating AI into our existing products that you love. Visit https://www.cladfy.com/copilot to explore our current use cases and learn more about getting started.

Conclusion:

By embracing Gen AI responsibly and strategically, MFIs can unlock a new era of operational efficiency, enhanced customer service, and financial inclusion. Cladfy is committed to exploring the responsible application of Gen AI to empower MFIs and contribute to a more inclusive financial ecosystem.

Forget tedious manual categorization! Let Cladfy automatically extract, classify, and compute transactions in bank statements, saving you hours of work. Imagine:

• AI-powered intelligence: Cladfy learns from your historical data and industry trends to accurately categorize income, expenses, and other transactions.

• Customizable categories: Tailor categories to your specific needs, ensuring accurate data for analysis and reporting.

• Drill down for deeper insights: Easily analyze specific categories or groups of transactions to gain a comprehensive understanding of borrower finances.

  Loan Ability Estimates:

Go beyond simple income verification. Cladfy leverages extracted data to generate preliminary loan ability estimates in minutes. This empowers you to:

• Pre-qualify borrowers quickly: Streamline the initial application process by providing fast, data-driven loan feasibility assessments.

• Set clear expectations: Give borrowers a realistic picture of their borrowing potential, fostering trust and transparency.

• Focus on qualified leads: Allocate resources efficiently by prioritizing applications with higher chances of approval.

  Seamless Integration:

The Cladfy software integrates with your existing lending management system (LMS), eliminating the need for disruptive changes:

• No complex setup: Our pre-built connectors ensure smooth integration with popular LMS platforms.

• Data flows effortlessly: Transactions data automatically populates relevant fields in your LMS, saving time and reducing errors.

• Centralized data management: Access and analyze all loan data within your familiar LOS environment.

  Scalable and Secure:

Cladfy is built to handle your growing needs and protect sensitive borrower information:

• Cloud-based scalability: Our infrastructure scales automatically to accommodate increasing data volumes and user demands.

• Advanced security measures: Industry-leading security protocols safeguard your data, ensuring compliance and peace of mind.

• Regular audits and penetration testing: We continuously evaluate and strengthen our security posture to stay ahead of threats.

In short, Cladfy doesn’t give you just another data extraction tool; you get a comprehensive solution designed to transform your loan underwriting process. This gives you the power to confidently approve more loans and delight your borrowers.

Where is the change?

This new data source, i.e. Alternative Data, is proving to be the most significant input that will differentiate the next few years from what we have seen in the past.

By leveraging consumer permission to access one of the richest data sources of underwriting, the checking/transactions account data, lenders can control more variables as they serve new segments of borrowers.

What should a lender do?

• Invest in technology and expertise.

• Create flexible underwriting models.

• Embrace transparency and ethical practices

By anticipating this change and partnering with an established player, credit issuers can speed up the transition to implementing Cash-flow data Analysis in their underwriting processes.

Conclusion

By embracing this revolution, lenders can unlock expanded reach, accurate decisions, better portfolios, and most importantly, the trust of borrowers and regulators in this exciting new era of credit.

Let us help you get ahead of the curve using our software solutions for cash-flow data analysis;

• Email us at info@cladfy.com

• See our API Docs at https://cladfy.readme.io/

Imagine your customers: Financially empowered, confident, and loyal. We are talking about effortless tracking of patterns in Income, Spending, Savings, Borrowing, Gambling, P2P transactions... to inform budgeting and investing decisions for individuals and/or SMEs.

What is new?

As you are aware, customers crave personalized financial insights, and that's why we are Introducing Embedded Cashflow Analysis, the new PFM game-changer that brings powerful financial insights into your existing app 🎉. This isn't just a feature; it's a revolution in customer empowerment, engagement, and retention.

The goal is to unlock the hidden insights within Bank and Mobile Money Statements, giving users a real-time, categorized view of their spending and saving habits across different banks and mobile money wallets.

Here's how Cashflow Analysis transforms your customers' lives:

1. Financial Superpower: Turn multiple bank and mobile money statements into actionable insights. Automatic transaction categorization, real-time spending patterns, and personalized budgeting tools empower your customers to take control of their finances.

2. Goal-Crushing Confidence: Gamified savings goals, investment recommendations tailored to risk tolerance, and market updates keep customers motivated and informed. Watch their financial confidence soar as they achieve their dreams.

3. Frictionless Integration: Seamlessly integrate Cashflow Analysis within your app, with no need for clunky downloads or confusing interfaces. Your customers get a unified financial experience, boosting satisfaction and loyalty.

4. Brand Loyalty Magnet: Become the go-to app for financial well-being. Cashflow Analysis differentiates your brand, attracting and retaining customers who value financial empowerment.

But the benefits go beyond customer love:

• New Revenue Stream: Introduce paid access to the statements analysis service as a premium feature for the insights generated.

• Data-Driven Decisions: Gain valuable insights via your customers to inform your company's product development and personalize marketing campaigns for maximum impact.

• Enhanced Brand Reputation: Championing financial literacy positions your brand as a trusted advisor, fostering deeper customer relationships and boosting brand image.

• Competitive Edge: Stand out in the crowded app market. Cashflow Analysis sets you apart, attracting tech-savvy customers who demand financial control.

Ready to unlock the full potential of your app?

Cashflow Analysis is the key. We offer a flexible, scalable solution that integrates seamlessly with your existing platform.

Don't wait for your customers to seek financial solutions elsewhere. Embed Cashflow Analysis today and empower them to achieve their financial goals, one informed decision at a time.

Contact us to learn more and get started with the integration.

• Email us at info@cladfy.com

• API Docs at https://cladfy.readme.io/

Remember, your customers' financial success is your success. Let's build a future where everyone thrives, one insightful app at a time.

When it comes to credit profiling and credit scoring, a substantial population has continuously been unintentionally overlooked by traditional systems. These individuals —the 'Invisibles', 'no hits', 'thin files',... often lack a footprint in credit bureaus, rendering them ineligible for standardized processes that heavily rely on non-inclusive credit scores. In response to this challenge, Cladfy boldly steps into the fray, leveraging the power of cash flow data to meticulously assess the repayment capacity and credit affordability of these consumers. In doing so, Cladfy opens doors that were once firmly closed due to the absence of a conventional credit score.

The Holy Grail of Credit Issuance:

A New Paradigm Emerges!

For credit issuers navigating the dynamic financial landscape, the main pillars of success have always included;

• A clean credit data set,

• A 360-degree view of the customer, and

• The technological prowess to seamlessly integrate these elements.

The emergence of cash flow underwriting marks a transformative shift in this paradigm. Not only does it provide real-time credit information, but it also offers a holistic perspective on the customer, presenting a significant leap forward in the industry. This shift equips lenders with the ability to expand their credit boxes and enhance their overall underwriting capabilities.

Cash Flow Underwriting in Action:

From Leading Practice to Standard Practice!

Recent industry developments have propelled cash flow underwriting from being a leading practice to establishing itself as a standard practice. Its effectiveness is unmistakable, as it not only assesses additional information beyond the confines of traditional credit bureau data but also adapts seamlessly to the evolving financial landscape.

Open banking, with its enhanced data accessibility, further fortifies the position of lenders employing cash flow underwriting. These forward-thinking lenders gain a distinct competitive edge by obtaining a comprehensive, forward-looking view of a customer's ability to meet financial obligations.

Navigating the Current Landscape:

The Crucial Role of Cash Flow Data!

In the aftermath of the Covid era, credit scores derived from historic performance data face unprecedented challenges. New financial products, such as 'buy now, pay later,' introduce a layer of complexity to conventional credit spaces. Recognizing this, cash flow data emerges as a necessary supplement, providing a more versatile and real-time perspective on a customer's financial profile. This invaluable tool not only addresses the challenges posed by historical data tarnished by pandemic-related events but also positions lenders to navigate the complexities introduced by innovative financial products.

Cladfy's Role in Portfolio Management:

Revolutionizing Consumer Insights!

Beyond the initial credit assessment, Cladfy's platform offers lenders and issuers a robust tool for portfolio management. Monitoring consumer performance in almost real-time, post-origination provides invaluable insights into consumer behavior. The unique value of cash flow data lies in its recency, offering prompt assessments of payment capabilities. This not only enhances the precision of portfolio management but also revolutionizes the industry's approach to assessing and responding to consumer financial behavior.

Regulatory Framework Overview:

Navigating the Future Landscape!

As the financial industry evolves, the integration of cash flow data in credit underwriting necessitates a nuanced understanding of the regulatory landscape.

Key considerations include;

• Real-time access to data,

• The permissible extent of its utilization in developing credit policies, and

• Adherence to evolving regulatory frameworks to ensure compliance.

Addressing these regulatory aspects is crucial for ensuring that the integration of cash flow data aligns with industry standards and legal requirements.

The Inevitable Future:

1. Beyond Credit Bureau Data

   As the industry continues to evolve, the necessity of bureau data becomes apparent, but not sufficient. Cash flow data emerges as an indispensable component for obtaining a comprehensive 360-degree view of a customer's credit profile. While the industry acknowledges the need for data beyond traditional credit bureau information, the challenges lie in standardization and infrastructure. Yet, as the regulatory framework evolves, lenders can anticipate streamlined processes for obtaining cash flow data, fostering wider acceptance and integration.

2. Democratization of Data: Paving the Way Forward

   As regulatory frameworks evolve, the democratization of data stands as a significant milestone. Standardization and infrastructure associated with cash flow data facilitate safe and easy consumer permission processes. This not only ensures compliance but also makes the provision of data a more widely accepted and streamlined aspect of the credit application process. The evolving landscape envisions a future where access to data becomes more equitable, ensuring that even those at the bottom of the socioeconomic pyramid have the opportunity to participate in the financial ecosystem.

3. Automation of Workflows: Efficiency Redefined

   Integrating cash flow data in a clever and automated manner has the potential to yield substantial positive impacts. A significant increase in conversion rates and an improvement in credit quality are foreseeable outcomes, benefiting issuers and enhancing the overall efficiency of credit operations. The industry stands at the cusp of a transformative era where automated workflows, guided by cash flow data, redefine how credit operations are conducted, leading to increased efficiency and improved outcomes.

Conclusion:

Shaping the Future of Credit Underwriting!

As the financial landscape undergoes a profound transformation, embracing cash flow data for credit underwriting emerges not merely as a choice but as a pivotal strategy. Cladfy, at the forefront of this revolution, not only facilitates connections between micro-lenders and financing partners but also empowers lenders to make informed decisions. By extending credit to previously underserved populations, Cladfy plays a central role in reshaping the future of credit underwriting.

The fusion of regulatory awareness and technological innovation is paving the way for a dynamic landscape where cash flow data becomes a cornerstone in reshaping the credit industry. As the industry evolves, the democratization of data ensures that access becomes more equitable, and the automation of workflows promises increased efficiency and improved outcomes. In this era of transformation, Cladfy stands as a beacon, guiding the financial industry toward a future where financial inclusion is not just a goal but a tangible reality for all.

A state machine is like a flowchart that helps describe and control how things work. It shows the different steps something can be in, how it moves from one step to another, and what makes it move.

State machines are important tools in software development where they help in understanding and managing how different parts of a system or process behave.

In a Lending Management System (LMS) a Loan State Machine is used to model the different states and transitions that a loan application goes through as it progresses from submission to approval or rejection, repayments, and closure.

Below is a snapshot of a state machine inside the Cladfy Lending Management System. It has become instrumental in visualizing, tracking, and managing the different states of a loan application through its lifecycle.

Note: You can add additional states and transitions as needed to capture the specific workflow and business rules of your loan application process.

Closer Look.

Imagine a loan application as a journey with different steps. A state machine for a loan application helps keep track of where each application is in the process and what actions are needed to move it forward. Here's how it works:

• States: Think of these as the different stages that a loan application can be in. For instance, it can start in the "Application Submitted" state.

• Transitions: These are like the routes or paths that an application can take from one stage to another. For example, from "Application Submitted," it can transition to "Under Review" when a loan officer starts looking at it.

• Events: Events are like the triggers that make an application move from one stage to another. For a loan application, an event could be something like "Loan Officer Review," which moves it from "Application Submitted" to "Under Review."

Example.

Here's a simplified version of a loan application state machine:

1. Application Submitted: This is the starting point when a customer applies for a loan.

2. Under Review: The application is being evaluated by a loan officer.

3. Approved: If the application meets all the criteria, it moves to the "Approved" state.

4. Rejected: If the application doesn't meet the requirements, it goes to the "Rejected" state.

5. Funded: After approval, the loan is funded, and the application reaches the "Funded" state.

6. Closed: When the loan is repaid, the application is marked as "Closed."

Each of these states and transitions helps a lender understand where each application stands in the process. It ensures that the right actions are taken at the right time, making the loan approval process more organized and efficient.

Conclusion.

Discover and leverage the power of state machines in technology and finance to simplify your lending operations today at www.cladfy.com

Repayment scores are typically used by lenders to make decisions about whether to approve a loan and what interest rate to charge.

Repeat clients are borrowers who have previously borrowed money from a lender and have repaid their loans in full and on time. These borrowers are considered to be lower-risk borrowers, and lenders may be willing to offer them more favorable terms on future loans, such as a lower interest rate or a longer repayment term.

One way to reward repeat clients and encourage them to continue borrowing from a lender is to offer them a higher repayment score. This would give them access to more favorable loan terms, such as a lower interest rate or a longer repayment term.

To calculate a repayment score for repeat clients:

• Give more weight to recent payments. Borrowers who have made recent payments on time are less likely to default on a new loan, so their repayment score should be higher.

• Consider the number of loans repaid. Borrowers who have repaid multiple loans from the same lender are less likely to default on a new loan, so their repayment score should be higher.

• Factor in the size of the loans repaid. Borrowers who have repaid larger loans from the same lender are less likely to default on a new loan, so their repayment score should be higher.

• Give credit for early repayment. Borrowers who have repaid their loans early are less likely to default on a new loan, so their repayment score should be higher.

By offering repeat clients a higher repayment score, lenders can encourage them to continue borrowing from the lender and reduce the risk of default.

Some of the major parameters that go into the calculations of a repayment score are:

• Repayment Start-Date

• Repayment End-Date

• Expected repayment Day

• Expected repayment Amount on each repayment Day

• Time intervals in the repayments of an active or an overdue loan

Below is a practical look into the Time Intervals (Streaks)

1. paidStreak

This number represents the longest continuous streak of on-time (fully paid) payments. E.g. if on-time payments were made 5 months in a row, this number will be 5.

2. overDueStreak

This number represents the longest continuous overdue number of payments. E.g. if payments were overdue for 2 months in a row, this number will be 2.

Example

Here is an example of how these variables can be used to assess a borrower's creditworthiness:

Borrower A: paidStreak = 12 overDueStreak = 0

Borrower B: paidStreak = 6 overDueStreak = 1

Based on this information, we can conclude that Borrower A has a better credit score than Borrower B. Borrower A has a longer streak of on-time payments and no overdue payments, while Borrower B has a shorter streak of on-time payments and one overdue payment.

The paidStreak and overDueStreak fields can be useful metrics for borrowers to track their creditworthiness and identify areas where they can improve. For example, a borrower with a short paid streak may want to focus on making all of their payments on time and in full.

It is important to note that the paidStreak and overDueStreak fields are just two of many factors lenders consider when making lending decisions. Other factors include;

1. Credit score: A credit score is a three-digit number that represents a person's creditworthiness. It is calculated using information from a person's credit report, such as their payment history, amount of debt, and length of credit history. A higher credit score indicates a lower risk of default, and lenders are more likely to approve loans for borrowers with high credit scores.

2. Debt-to-income ratio (DTI): The DTI ratio is a measure of how much debt a person has relative to their income. It is calculated by dividing the total monthly debt payments by the total monthly income. A lower DTI ratio indicates that a person has more disposable income to make loan payments, and lenders are more likely to approve loans for borrowers with low DTI ratios.

3. Employment history: Lenders also want to see that a borrower has a stable job and income. They will typically look at the borrower's employment history for the past two to three years. A borrower with a long and stable employment history is considered to be a lower risk, and lenders are more likely to approve loans for these borrowers.

4. Purpose of the loan

5. Type of loan

6. Collateral

7. Down payment

Lenders will use all of this information to assess a borrower's risk and determine whether to approve the loan.

Conclusion

It is important to note that the weight that lenders give to each factor can vary depending on the lender.

• For example, some lenders may place a greater emphasis on credit score, while others may place a greater emphasis on the DTI ratio.

Borrowers can improve their chances of getting approved for a loan by improving their credit score, reducing their DTI ratio, and having a stable cashflow.

See BFAGlobal report -NPLs for active digital credit from Fintechs is 66% in Kenya 2019

See CBK Credit Officer Survey March 31, 2023

• They are faster. Informal lenders can often process loan applications and disburse funds more quickly than formal lenders. This is important for people who need money urgently, such as to cover unexpected expenses or to start a business.

• They are more flexible. Informal lenders are often more flexible than formal lenders when it comes to the terms of loans. For example, they may be willing to lend to people with poor credit histories or who do not have collateral. They may also be willing to lend smaller amounts of money, which can be helpful for people who are starting or who have limited financial resources.

By tracking these metrics, lenders can gain insights into their lending operations and make informed decisions that will help them improve their performance.

Want to learn more about the 5 process-based metrics every lender must track? Contact us today!