In 2023, the delinquency rates of all types of mortgage lenders in Canada increased. As of the fourth quarter of the year, approximately 1.05 percent of loans in the loan portfolios of mortgage investment entities (MIEs) were classified as delinquent, which was a decrease from the 0.78 percent delinquency rate a year ago. A loan is reported by lenders as being delinquent after 270 days of late payments.

The share of mortgages in arrears in Canada reached an all-time low in 2022, followed by an increase until 2025. As of **********, the rate of mortgage arrears was **** percent, up from **** percent in September 2022.

Following the drastic increase directly after the COVID-19 pandemic, the delinquency rate started to gradually decline, falling below *** percent in the second quarter of 2023. In the second half of 2023, the delinquency rate picked up but remained stable throughout 2024. In the second quarter of 2025, **** percent of mortgage loans were delinquent. That was significantly lower than the **** percent during the onset of the COVID-19 pandemic in 2020 or the peak of *** percent during the subprime mortgage crisis of 2007-2010. What does the mortgage delinquency rate tell us? The mortgage delinquency rate is the share of the total number of mortgaged home loans in the U.S. where payment is overdue by 30 days or more. Many borrowers eventually manage to service their loan, though, as indicated by the markedly lower foreclosure rates. Total home mortgage debt in the U.S. stood at almost ** trillion U.S. dollars in 2024. Not all mortgage loans are made equal ‘Subprime’ loans, being targeted at high-risk borrowers and generally coupled with higher interest rates to compensate for the risk. These loans have far higher delinquency rates than conventional loans. Defaulting on such loans was one of the triggers for the 2007-2010 financial crisis, with subprime delinquency rates reaching almost ** percent around this time. These higher delinquency rates translate into higher foreclosure rates, which peaked at just under ** percent of all subprime mortgages in 2011.

This table contains 102 series, with data starting from 2013, and some select series starting from 2016. This table contains data described by the following dimensions (Not all combinations are available): Geography (1 item: Canada), Components (51 items: Total, funds advanced, residential mortgages, insured; Variable rate, insured; Fixed rate, insured, less than 1 year; Fixed rate, insured, from 1 to less than 3 years; ...), and Unit of measure (2 items: Dollars; Interest rate). For additional clarification on the component dimension, please visit the OSFI website for the Report on New and Existing Lending.

Percent of British Columbia Student Loan borrowers from British Columbia's private post-secondary institutions who have consolidated their loans and failed to fulfill repayment responsibilities. Data available by institution. Five-year consolidation cohorts, Fiscal Years 2003/04 - 2007/08 to 2007/08 - 2011/12.

🏦 Synthetic Loan Approval Dataset

A Realistic, High-Quality Dataset for Credit Risk Modelling

🎯 Why This Dataset?

Most loan datasets on Kaggle have unrealistic patterns where:

1. ❌ Credit scores don't matter
2. ❌ Approval logic is backwards
3. ❌ Models learn nonsense patterns

Unlike most loan datasets available online, this one is built on real banking criteria from US and Canadian financial institutions. Drawing from 3 years of hands-on finance industry experience, the dataset incorporates realistic correlations and business logic that reflect how actual lending decisions are made. This makes it perfect for data scientists looking to build portfolio projects that showcase not just coding ability, but genuine understanding of credit risk modelling.

📊 Dataset Overview

🔑 Key Features

Identifier:

-Customer ID (unique identifier for each application)

Demographics:

-Age, Occupation Status, Years Employed

Financial Profile:

-Annual Income, Credit Score, Credit History Length -Savings/Assets, Current Debt

Credit Behaviour:

-Defaults on File, Delinquencies, Derogatory Marks

Loan Request:

-Product Type, Loan Intent, Loan Amount, Interest Rate

Calculated Ratios:

-Debt-to-Income, Loan-to-Income, Payment-to-Income

💡 What Makes This Dataset Special?

1️⃣ Real-World Approval Logic The dataset implements actual banking criteria: - DTI ratio > 50% = automatic rejection - Defaults on file = instant reject - Credit score bands match real lending thresholds - Employment verification for loans ≥$20K

2️⃣ Realistic Correlations - Higher income → Better credit scores - Older applicants → Longer credit history - Students → Lower income, special treatment for small loans - Loan intent affects approval (Education best, Debt Consolidation worst)

3️⃣ Product-Specific Rules - Credit Cards: More lenient, higher limits - Personal Loans: Standard criteria, up to $100K - Line of Credit: Capped at $50K, manual review for high amounts

4️⃣ Edge Cases Included - Young applicants (age 18) building first credit - Students with thin credit files - Self-employed with variable income - High debt-to-income ratios - Multiple delinquencies

🎓 Perfect For - Machine Learning Practice: Binary classification with real patterns - Credit Risk Modelling: Learn actual lending criteria - Portfolio Projects: Build impressive, explainable models - Feature Engineering: Rich dataset with meaningful relationships - Business Analytics: Understand financial decision-making

📈 Quick Stats

Approval Rates by Product - Credit Card: 60.4% more lenient) - Personal Loan: 46.9 (standard) - Line of Credit: 52.6% (moderate)

Loan Intent (Best → Worst Approval Odds) 1. Education (63% approved) 2. Personal (58% approved) 3. Medical/Home (52% approved) 4. Business (48% approved) 5. Debt Consolidation (40% approved)

Credit Score Distribution - Mean: 644 - Range: 300-850 - Realistic bell curve around 600-700

Income Distribution - Mean: $50,063 - Median: $41,608 - Range: $15K - $250K

🎯 Expected Model Performance

With proper feature engineering and tuning: - Accuracy: 75-85% - ROC-AUC: 0.80-0.90 - F1-Score: 0.75-0.85

Important: Feature importance should show: 1. Credit Score (most important) 2. Debt-to-Income Ratio 3. Delinquencies 4. Loan Amount 5. Income

If your model shows different patterns, something's wrong!

🏆 Use Cases & Projects

Beginner - Binary classification with XGBoost/Random Forest - EDA and visualization practice - Feature importance analysis

Intermediate - Custom threshold optimization (profit maximization) - Cost-sensitive learning (false positive vs false negative) - Ensemble methods and stacking

Advanced - Explainable AI (SHAP, LIME) - Fairness analysis across demographics - Production-ready API with FastAPI/Flask - Streamlit deployment with business rules

⚠️ Important Notes

This is SYNTHETIC Data - Generated based on real banking criteria - No real customer data was used - Safe for public sharing and portfolio use

Limitations - Simplified approval logic (real banks use 100+ factors) - No temporal component (no time series) - Single country/currency assumed (USD) - No external factors (economy, market conditions)

Educational Purpose This dataset is designed for: - Learning credit risk modeling - Portfolio projects - ML practice - Understanding lending criteria

NOT for: - Actual lending decisions - Financial advice - Production use without validation

🤝 Contributing

Found an issue? Have suggestions? - Open an issue on GitHub - Suggest i...

United States - Delinquency Rate on Single-Family Residential Mortgages, Booked in Domestic Offices, All Commercial Banks was 1.78% in July of 2025, according to the United States Federal Reserve. Historically, United States - Delinquency Rate on Single-Family Residential Mortgages, Booked in Domestic Offices, All Commercial Banks reached a record high of 11.49 in January of 2010 and a record low of 1.41 in October of 2004. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Delinquency Rate on Single-Family Residential Mortgages, Booked in Domestic Offices, All Commercial Banks - last updated from the United States Federal Reserve on December of 2025.

Monthly credit aggregates for the household sector, by category.

Student Loan Defaults and Rehabilitations. 'Rehabilitated' means getting a loan back into good standing, i.e. out of default. 2016-17 is the first fiscal year that rehabilitation of loans was implemented.

Student Loan Defaults and Rehabilitations. 'Rehabilitated' means getting a loan back into good standing, i.e. out of default. 2016-17 is the first fiscal year that rehabilitation of loans was implemented.

In 2018, Mortgage Investment Corporations (MICs) in Canada had an average lending rate of *** percent. MICs are investment vehicles which allow their shareholders to collectively invest in a large pool of residential and commercial mortgage investments. They typically have higher delinquency rates than traditional lenders such as banks and credit unions.

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Loan Servicing Software Market Size 2025-2029

The loan servicing software market size is forecast to increase by USD 3.43 billion, at a CAGR of 13.4% between 2024 and 2029.

The market is driven by the increasing demand for efficiency in lending operations. Lenders seek to streamline their processes and reduce operational costs, making automated loan servicing solutions increasingly valuable. Strategic partnerships and acquisitions among market participants further fuel market expansion, as they collaborate to offer comprehensive solutions and expand their reach. Creditworthiness is assessed using credit scoring algorithms, alternative data sources, and AI, ensuring lenders mitigate default risk. However, the market faces challenges from open-source loan servicing software, which can offer cost-effective alternatives to proprietary solutions.
As competition intensifies, companies must differentiate themselves through superior functionality, customer service, and integration capabilities to maintain market share. To capitalize on opportunities and navigate challenges effectively, market players should focus on continuous innovation, strategic partnerships, and robust customer support.

What will be the Size of the Loan Servicing Software Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The market continues to evolve, driven by the need for system scalability, regulatory reporting, and enhanced user experiences. Loan servicers seek solutions that seamlessly integrate escrow management, automated payment processing, machine learning, and predictive analytics. Hybrid loan servicing models, which combine on-premise and cloud-based systems, are gaining popularity. Loan portfolio management, loan servicing workflow, and loan origination systems are key areas of focus. Mobile loan servicing and loan servicing consulting are also important, as servicers strive for increased efficiency and improved customer communication management. Risk management, data migration, API integration, and document management are essential components of modern loan servicing solutions.

Default management, foreclosure management, and audit trail are also critical, ensuring regulatory compliance and data integrity. Loan servicing reporting, fraud detection, and loan servicing analytics are crucial for effective decision-making. User experience and loan servicing training are also prioritized, as servicers aim to provide exceptional customer satisfaction. Artificial intelligence and machine learning are transforming loan servicing, enabling predictive analytics and automated loan modification processing. Regulatory reporting and system scalability remain top priorities, as servicers navigate the evolving loan servicing landscape.

How is this Loan Servicing Software Industry segmented?

The loan servicing software industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Application

  Banks
  Credit unions
  Mortgage lenders
  Brokers
  Others


Deployment

  Cloud-based
  On-premises


Component

  Software
  Services


Sector

  Large enterprises
  Small and medium enterprises


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan
    South Korea


  Rest of World (ROW)

By Application Insights

The banks segment is estimated to witness significant growth during the forecast period.

Loan servicing software is a crucial component of loan origination and servicing technologies (LOS) utilized by banks and financial institutions (BFSI). This software streamlines daily operations by enabling BFSI to accept loan applications online through their websites. The convenience of digital applications aligns with customers' preferences for using the Internet and smartphones. LOS solutions offer features such as EMI calculators, loan eligibility ready reckoners, and document checklists, facilitating a seamless application process 24/7. Pre-configured workflows for credit scoring, document checklist, and approvals significantly reduce turnaround time, enhancing operational efficiency by up to 50%. Escrow management, automated payment processing, and loan portfolio management are integral functions of loan servicing software.

Machine learning and predictive analytics optimize risk management, while user experience and document management ensure customer satisfaction. Cloud-based loan servicing and mobile loan servicing cater to the evolving needs of customers. Loan servicing consulting and automation services help institutions optimize their loan servicing processes.

Data on the number of OSAP loan recipients who received repayment assistance: * 2010-11 OSAP loan recipients who received repayment assistance before July 2013. * 2011-12 OSAP loan recipients who received repayment assistance before July 2014. * 2012-13 OSAP loan recipients who received repayment assistance before July 2015. * 2013-14 OSAP loan recipients who received repayment assistance before July 2016. * 2014-15 OSAP loan recipients who received repayment assistance before July 2017. * 2015-16 OSAP loan recipients who received repayment assistance before July 2018. * 2016-17 OSAP loan recipients who received repayment assistance before July 2019. * 2017-18 OSAP loan recipients who received repayment assistance before July 2020. Data is presented at the following levels: * all of Ontario * postsecondary sector * individual postsecondary institution * individual program of individual postsecondary institution Data fields are: * postsecondary sector (university, college of applied arts and technology, private career college, other private or publicly funded postsecondary institutions) * institution name * program name (starting with the 2014 rates) * number of OSAP loan recipients who last received an OSAP loan in 2010-11, 2011-12, 2012-13, 2013-14, 2014-15, 2015-16, 2016-17, and 2017-18 * number of Repayment Assistance Plan participants as of July 2013, July 2014, July 2015, July 2016, July 2017, July 2018, July 2019, and July 2020 * repayment assistance participation rate for 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020 Get more information about OSAP loan default rates. *[OSAP]: Ontario Student Assistance Program

Data on the number of OSAP loan recipients who received repayment assistance: * 2010-11 OSAP loan recipients who received repayment assistance before July 2013. * 2011-12 OSAP loan recipients who received repayment assistance before July 2014. * 2012-13 OSAP loan recipients who received repayment assistance before July 2015. * 2013-14 OSAP loan recipients who received repayment assistance before July 2016. * 2014-15 OSAP loan recipients who received repayment assistance before July 2017. * 2015-16 OSAP loan recipients who received repayment assistance before July 2018. * 2016-17 OSAP loan recipients who received repayment assistance before July 2019. * 2017-18 OSAP loan recipients who received repayment assistance before July 2020. Data is presented at the following levels: * all of Ontario * postsecondary sector * individual postsecondary institution * individual program of individual postsecondary institution Data fields are: * postsecondary sector (university, college of applied arts and technology, private career college, other private or publicly funded postsecondary institutions) * institution name * program name (starting with the 2014 rates) * number of OSAP loan recipients who last received an OSAP loan in 2010-11, 2011-12, 2012-13, 2013-14, 2014-15, 2015-16, 2016-17, and 2017-18 * number of Repayment Assistance Plan participants as of July 2013, July 2014, July 2015, July 2016, July 2017, July 2018, July 2019, and July 2020 * repayment assistance participation rate for 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020 Get more information about OSAP loan default rates. *[OSAP]: Ontario Student Assistance Program

We use new administrative data that links detailed information on Canadian student loan recipients with their repayment and income histories from the Canada Student Loans Program (CSLP), income tax filings, and post-secondary schooling records to measure the extent to which student borrowers adjust loan repayments to insure against income variation. Several mechanisms are available for students to adjust loan repayments in response to income fluctuations: formal, like CSLP's Repayment Assistance Plan; and informal, such as delinquency or default. Borrowers can also make larger payments than required should they experience unexpectedly high income. Indeed, loan payments are shown to increase in income, more so in early years and for individuals with higher initial debt. More formally, we estimate that on average, an unexpected $1,000 change in year-over-year income is associated with a $30 change in loan payment: from a $50 change the year after graduation, declining to a $20 change 5 years after graduation. Loan repayments are also used to absorb income variation that is more permanent in nature: for borrowers whose income is consistently below or above expected income at graduation, the magnitude of average repayment adjustment is similar to the average yearly response.

Snapshot img

Debt Settlement Market Size 2024-2028

The debt settlement market size is forecast to increase by USD 5.07 billion at a CAGR of 10.3% between 2023 and 2028.

The market is experiencing significant growth due to the increasing trend of consumers seeking relief from mounting credit card debts. One-time debt settlement has gained popularity as an effective solution for individuals looking to reduce their outstanding debt balances. However, the time-consuming nature of negotiations between debtors and creditors poses a challenge for market expansion. Despite this, the market's strategic landscape remains favorable for companies offering debt settlement services. Key drivers include the rising number of consumers struggling with debt, increasing awareness of debt settlement as a viable debt relief option, and the growing preference for affordable and flexible debt repayment plans.
Companies seeking to capitalize on market opportunities should focus on streamlining the negotiation process, leveraging technology to enhance customer experience, and building trust and transparency with clients. Effective operational planning and strategic partnerships with creditors can also help companies navigate the challenges of a competitive and complex market.

What will be the Size of the Debt Settlement Market during the forecast period?

Request Free Sample

The market encompasses a range of companies offering financial wellness programs to help consumers manage and reduce their debt. These programs include medical Debt collection, consumer debt relief, and financial education resources. Online financial resources and debt management software are increasingly popular, providing consumers with affordable debt solutions and debt negotiation strategies. However, it's crucial for consumers to be aware of debt settlement scams and their settlement success rates. Debt consolidation loans and financial planning tools are also viable options for responsible debt management. Furthermore, financial literacy education and workshops are essential for consumers to understand debt reduction calculators and credit reporting errors.
Consumer financial protection agencies offer financial counseling services and financial planning advice to promote financial wellness strategies and responsible borrowing. Student loan forgiveness programs are also gaining traction in the market. Overall, the market for debt settlement and financial wellness solutions continues to evolve, with a focus on providing accessible and effective debt relief options for consumers.

How is this Debt Settlement Industry segmented?

The debt settlement industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Type

  Credit card debt
  Student loan debt
  Medical debt
  Auto loan debt
  Unsecured personal loan debt
  Others


End-user

  Individual
  Enterprise
  Government


Distribution Channel

  Online
  Offline
  Hybrid


Service Type

  Debt Settlement
  Debt Consolidation
  Debt Management Plans
  Credit Counseling


Provider Type

  For-profit Debt Settlement Companies
  Non-profit Credit Counseling Agencies
  Law Firms
  Financial Institutions


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  Middle East and Africa



  APAC

    China
    India
    Japan
    South Korea


  South America



  Rest of World (ROW)

By Type Insights

The credit card debt segment is estimated to witness significant growth during the forecast period.

The market experiences significant activity due to the escalating credit card debt among consumers. In India, for instance, the rising financial hardships faced by borrowers are evident in the increasing credit card defaults. The latest data indicates that credit card defaults in India reached 1.8% in June 2024, a notable increase from 1.7% six months prior and 1.6% in March 2023. This trend underscores the mounting financial pressures on consumers. The outstanding credit card debt in India mirrors this trend, with approximately USD3.25 billion in outstanding balances as of June 2024, a slight increase from the previous year.

Debt elimination and negotiation strategies, such as debt relief programs and debt consolidation, have become increasingly popular among consumers seeking financial relief. Credit reporting agencies play a crucial role in this process, as they maintain and report consumers' credit histories to lenders. Student loan debt, medical debt, tax debt, and payday loans are other significant contributors to the market. Consumers often turn to debt validation, credit repair, and financial coaching for guidance in managing their debts. Online platforms, mobile apps, and budgeting tools have become esse

Annual aggregate data on payday loans granted in Nova Scotia as reported by lenders. Data includes information on total loans granted, average size of loans, and information on defaults and repeat lending.