Our initial sample comprises Chinese A-share listed firms on the Shanghai and Shenzhen stock exchanges from 2010 to 2020. Following Clement and Tse (2005) and accounting for institutional features of Chinese markets, we apply these exclusion criteria: (1) financial sector firms; (2) firms under Special Treatment designation due to abnormal financial conditions; (3) observations with missing variables; (4) firm-years covered by only one analyst; (5) firms experiencing bond defaults themselves; and (6) earnings forecasts issued within 30 calendar days preceding annual report disclosures. The final sample consists of 44,792 firm-year-quarter observations and 340,312 analyst earnings forecasts. The bond default data are obtained from the Wind database. The earliest year for corporate bond default in China is 2014. At the end of 2020, 810 corporate bonds had defaulted in China, involving 213 companies across 57 industries, where the industry classification is based on the three-digit 2012 China Securities Regulatory Commission classification code. The analyst earnings forecast data and financial data are from the China Stock Market & Accounting Research database. All continuous variables are winsorized at the 1st and 99th percentiles to mitigate outlier influence.

The Score resolves the challenge of assessing a company's financial stability and likelihood of payment default. Facilitating business partner evaluation with one standard score anywhere in the world and from any sector.​ - Comprehensive coverage from unregistered businesses to multinational companies, irrespective of the industry. - Early warning, global applicability, and real-time responsiveness.

Dataset Structure and Components: Status Indicators: Mixture of current (9) and historical (6) assessments

Risk Classification System: The dataset employs a sophisticated 1-9 scoring scale that directly correlates with probability of default:

9: Highest financial stability (0.05% default probability) 8: Financial stability (0.15% default probability) 7: Above average stability (0.4% default probability) 6: Average stability (0.7% default probability) 3: Financial difficulties (4% default probability) 2: Critical financial situation (10% default probability) 1: Pre-insolvency indicators (25% default probability)

Practical Applications: This sample illustrates how financial risk assessment can be standardized and quantified to support business decision-making.

The scoring system provides: Clear quantification of default risk over a 12-month horizon Consistent risk evaluation metrics across diverse company profiles Objective benchmarks for credit limit determinations Framework for monitoring changes in financial stability over time

The scoring structure allows organizations to establish risk tolerance thresholds, automate approval workflows based on score ranges, and create standardized reporting for stakeholders. Note: This is sample data intended to demonstrate the structure and capabilities of a financial scoring system.

Learn More For a complete demonstration of our Score capabilities or to discuss how our system can be integrated with your existing processes, please visit https://business-information.coface.com/what-is-urba360 to request additional information.

In the context of domestic supply side structural reform, the market environment is complex and ever-changing, and corporate debt defaults occur frequently. It is necessary to establish a timely and effective financial distress warning model Most of the existing distress prediction models have not effectively solved problems such as imbalanced datasets, unstable selection of key prediction indicators, and randomness in sample matching, and are not suitable for the current complex and changing market conditions in China Therefore, this article uses the Bootstrap resampling method to construct 1000 research samples, and uses LASSO (Least absolute shrinkage and selection operator) variable selection technology to screen key predictive factors to construct a logit model for predicting ahead of 3 years. In the prediction stage, the samples are randomly cut and predicted 1000 times to reduce random errors The results indicate that the Logit dilemma prediction model constructed by combining Bootstrap sample construction method with LASSO has stronger predictive ability compared to the traditional application of "similar industry asset size" method In addition, the embedded Bootstrap Lasso logit model has better predictive performance than mainstream O-Score models and ZChina Score models, with an accuracy increase of 10%, and is more suitable for China's time-varying market. The model constructed in this article can help corporate stakeholders better identify financial difficulties and make timely adjustments to reduce corporate bond default rates or avoid corporate defaults

Regression results of default risk on interest rate.

This paper focuses on firms in which insiders pledge their shares as collateral for loans. By investigating a natural experiment—China’s enactment of provisions on share reductions that restrict pledge creditors’ cashing-out behavior—we find that pledging firms exhibited more conservative financial reporting after the implementation than non-pledging firms. This effect was pronounced in firms with a higher ratio of pledged shares, a longer maturation period of the pledged shares, and more concentrated pledge creditors. Additionally, we show that pledging firms increased their accounting conservatism after the shock, leading to a lower risk of margin calls and stock price crashes. The effect on accounting conservatism was stronger in firms with controlling pledgers or when the pledge creditors were banks. Our results remained consistent after we performed several robustness tests. These behaviors are economically logical because the provisions heighten creditors’ liquidity risk and the potential losses of loan default. Pledging shareholders embrace more accounting conservatism to mitigate creditors’ concerns about agency costs and avoid triggering margin calls. Our findings provide direct support that creditors have a real demand for accounting conservatism and highlight the impact of shareholder-creditor conflicts on the financial reporting policies of pledging firms.

Snapshot img

Factoring Market Size and Forecast 2025-2029

The factoring market size estimates the market to reach by USD 2570.8 billion, at a CAGR of 9.5% between 2024 and 2029.Europe is expected to account for 56% of the growth contribution to the global market during this period. In 2019 the domestic segment was valued at USD 2283.70 billion and has demonstrated steady growth since then.

    Report Coverage


    Details




    Base year


    2024




    Historic period

    2019-2023



    Forecast period


    2025-2029



    Market structure
    Fragmented



    Market growth 2025-2029


    USD 2570.8 billion






The market is experiencing significant growth due to the increasing demand for alternative financing solutions among Micro, Small, and Medium Enterprises (MSMEs). This trend is driven by the cash flow management challenges faced by MSMEs, which often result in a need for immediate access to capital. Another key driver is the advent of blockchain technology in factoring services, offering enhanced security, transparency, and efficiency. However, the market also faces challenges, including the lack of stringent regulatory frameworks for debt recovery mechanisms in developing countries.
This can create uncertainty and risk for factoring companies operating in these regions, necessitating careful strategic planning and risk management approaches. To capitalize on market opportunities and navigate challenges effectively, companies must stay informed of regulatory developments and invest in technological innovations to streamline processes and improve customer experience.

What will be the Size of the Factoring Market during the forecast period?

Request Free Sample

The market for factoring services continues to evolve, offering innovative solutions to businesses seeking improved cash flow and risk management. Portfolio management and asset-based lending are key applications, enabling companies to optimize their working capital and enhance liquidity. Early warning systems, contract review, and financial statement analysis are essential components of credit scoring and risk mitigation, ensuring timely identification of potential defaults and effective recovery rates. Invoice financing and purchase order financing provide businesses with immediate access to cash, while debt factoring allows for the sale of accounts receivable to a third party. Credit underwriting, transaction processing, and regulatory reporting are crucial aspects of the factoring process, ensuring compliance with legal and financial standards.

Data analytics plays a significant role in the market, providing insights into credit risk, liquidity management, and fraud detection. Collateral management and loss given default are essential elements of credit insurance, offering protection against potential losses. Due diligence and business valuation are integral parts of the factoring process, ensuring accurate and reliable assessments. The market is expected to grow at a robust rate, with industry experts projecting a significant increase in demand for these services. For instance, a leading manufacturing company experienced a 25% increase in sales after implementing invoice financing, highlighting the potential benefits of factoring solutions.

How is this Factoring Industry segmented?

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

Application

  Domestic
  International


Enterprise Size

  SMEs
  Large enterprise


Type

  Recourse
  Non-Recourse


End-User

  Manufacturing
  Transport & Logistics
  Information Technology
  Healthcare
  Construction
  Staffing
  Others


Provider

  Banks
  NBFCs


Geography

  North America

    US
    Canada
    Mexico


  Europe

    France
    Germany
    Italy
    Spain
    UK


  Middle East and Africa

    UAE


  APAC

    China
    India
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By Application Insights

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

In the dynamic business landscape, the market plays a significant role in providing short-term liquidity solutions to Small and Medium Enterprises (SMEs). With increasing demand for non-recourse financing among SMEs, the market has witnessed notable growth. Factoring offers SMEs various benefits, such as quick access to cash, debt security, and improved working capital management. The process involves the sale of accounts receivable to a third party, known as a factor, at a discount. This enables SMEs to receive immediate payment for their invoices, thereby improving their cash flow and reduci

Snapshot img

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.
Request Free Sample

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.

Performance comparison of the proposed model against advanced studies using the KMV default dataset.

Share pledge and accounting conservatism in pledging firms.

This paper focuses on firms in which insiders pledge their shares as collateral for loans. By investigating a natural experiment—China’s enactment of provisions on share reductions that restrict pledge creditors’ cashing-out behavior—we find that pledging firms exhibited more conservative financial reporting after the implementation than non-pledging firms. This effect was pronounced in firms with a higher ratio of pledged shares, a longer maturation period of the pledged shares, and more concentrated pledge creditors. Additionally, we show that pledging firms increased their accounting conservatism after the shock, leading to a lower risk of margin calls and stock price crashes. The effect on accounting conservatism was stronger in firms with controlling pledgers or when the pledge creditors were banks. Our results remained consistent after we performed several robustness tests. These behaviors are economically logical because the provisions heighten creditors’ liquidity risk and the potential losses of loan default. Pledging shareholders embrace more accounting conservatism to mitigate creditors’ concerns about agency costs and avoid triggering margin calls. Our findings provide direct support that creditors have a real demand for accounting conservatism and highlight the impact of shareholder-creditor conflicts on the financial reporting policies of pledging firms.

Comparative performance of the proposed model vs. best-existing models on CSMAR, MorningStar, KMV, GMSC, and UCICCD datasets.

The effect of controlling shareholders as pledgers.

Computational efficiency comparison of the models on the CSMAR and MorningStar dataset.

Comparison of machine learning and deep learning models for credit risk prediction.

Performance comparison of the proposed model against advanced and ablated studies using the CSMAR dataset.

Comparative analysis of computational efficiency across various hyperparameter optimization algorithms on the CSMAR and MorningStar datasets.

Performance evaluation of the proposed model on majority and minority classes for the CSMAR and MorningStar datasets.

Performance comparison of the proposed model against advanced studies using the GMSC dataset.

Hyperparameters and their ranges for optimization in the credit risk prediction method.

Performance comparison of the proposed model against advanced and ablated studies using the MorningStar dataset.