The mortgage industry is a vital part of the United Kingdom’s house transaction system. Mortgages and loans support over 70% of the households in the UK. However, the mortgage loan origination system isn’t limited to house loans alone.
Small and Medium Enterprises (SMEs) also require loans and mortgages. SME businesses utilize this loan to carry out and fulfil working expenses or use it as a long-term investment.
As of 2020, there are over 6 million SMEs in the UK. Statistics also show that from 2011 to 2018, the share of SME loans was 34.8% of all business loans sanctioned in the UK.
This blog discusses the issues and problems causing loan rejections for SME businesses and the role of Artificial Intelligence (AI) in improving the mortgage origination process.
SME Loan Origination Process
The SME loan origination process consists of numerous problems that are creating big holes in its ship.
Issue 1 – Lack of Documents
Like the housing mortgage origination system, SMEs also have to send in their applications which will then undergo the underwriting process by the lenders or underwriters.
Most SMEs spend days organizing their applications. Apart from the applications, supporting documents like bank statements, balance sheets, and documents indicating efficiency and performance are also needed.
But the biggest problem that SMEs face is the unavailability of proper and complete documents. Most SMEs either self-declare their finances or do not maintain their records. This makes it difficult for the lenders to check the creditworthiness of the borrower.
Issue 2 – Time-consuming
Even after the approval of applications, SMEs have to wait for days, if not weeks, for their loan approval requests to come through. On average, the time taken for completing loan approvals and funding in the UK is around 35 days.
Issue 3 - Manual Processing and Lack of Manpower
The lenders manually execute the entire mortgage origination process. Underwriters go through pages of documents to ensure that the SMEs satisfy all the requirements of the loan.
Due to external factors like downsizing, and layoffs, a qualified workforce also poses a problem. This is why few banks follow the same level of underwriting on all applications, regardless of their complexity and magnitude.
Both these reasons lead to errors in scrutinizing documents which eventually causes loan rejections.
A study performed on loan and credit application rejections in the UK from 2012 to 2018 found that the rejection levels fluctuated each year. The rejection rates were at 17% in 2018. The highest rejection rate was witnessed in 2013, when 16 out of every 50 applications were rejected.
Issue 4 - External Factors like COVID-19
The COVID-19 pandemic has also caused huge impacts on the UK mortgage market.
A study done by the Mortgage Market Tracker of the Intermediary Mortgage Lenders Association (IMLA) on the UK mortgage industry showed,
- A significant dip in the gross mortgage lending during the second quarter of 2020 when the pandemic had made its first blow.
- The conversions from proper applications to offers and completions had also witnessed a dip during the same period compared to other quarters.
Introducing Effective Solutions
With so many issues affecting SME businesses and the loan process, new approaches are needed.
The government introduced new UK mortgage lending schemes for SMEs that calmed the COVID-19 storm on the mortgage market.
Schemes like the Bounce Bank Loan Scheme (BBLS) and the Coronavirus Business Interruption Loan Scheme (CBILS) lead to double gross lending to SMEs in the first three quarters of 2020 than the entire amount lent in 2019. The loan approval rate also witnessed an increase in the second and third quarters of 2020.
Artificial Intelligence (AI) and Machine Learning (ML) models are powerful technologies that can solve the issues regarding SME loan origination. AI powered mortgage lending tools automate the entire underwriting process by scrutinizing the data in the documents. On the other hand, machine learning models enable the software to learn from the data and improve its efficiency.
Need for AI Incorporation
There is a distinct need for using an artificial intelligence in loan origination because of its ability to automate the entire process, thereby reducing operations costs.
By 2027, AI software and tools might reduce the administrative workload on staff by 2.4 hours a day for each employee in banking and by 2.9 hours a day for employees working in capital market firms.
Benefits of AI and ML
- Higher Efficiency
Due to SMEs’ lack of financial documents, a few lenders look into other alternative sources of information that can help make better loan approvals. These sources may include utility payments data or even industry-based information.
Loan origination platforms that use artificial intelligence and machine learning can complete this process faster due to their high processing plans. In addition, these technologies can process structured and unstructured documents and images.
Evidence suggests that AI and ML-based models are better at predicting losses and creditworthiness using non-conventional data sources when compared with traditional and manual underwriting methodologies.
2. High Processing Power
Take the Bounce Back Loan Scheme (BBLS) as an example. Over 69,000 bounce-back loans were approved by banks in the first 24 hours, equalling a total of over 2 billion pounds.
Apart from the government providing a 100% guarantee to lenders on these new schemes, reaching this humungous number was made possible because a few banks used machine learning to process the documents.
In fact, over two-thirds of financial service companies in the UK have started incorporating machine learning and artificial intelligence in their services.
- Extraction and Classification
Artificial Intelligence can extract the data from various sources and classify them according to their type. AI can also compare it with the loan requirements and regulations and provide meaningful insights that can help the lender make more effective decisions on the borrower’s creditworthiness.
Loan origination software companies like Digilytics AI help lenders scale and streamline the loan origination process.
RevEl, a product of Digilytics, revolutionizes SME mortgage origination by leveraging the power of artificial intelligence and machine learning. RevEl is capable of performing many tasks efficiently. A few of them are listed below.
- Organize and classify the documents based on their category using one-shot learning technology.
- Increase first- time- right applications through efficient data recognition and extraction.
- Check the completeness, correctness, and consistency of information in all documents.
- Assist underwriters by predicting creditworthiness.
- Provide real-time status and analytics to the lender.
Digilytics partnered with Decimal Factor, a company that aims at funding SME businesses. Decimal Factor implemented Digilytics™ RevEl application module: First of its kind API based solution for processing bank statements, leveraging AI & advanced OCR to streamline UK loan origination process.
Read full press release here
Download the full case study here
Digilytics AI also partnered with AccountScore, an Equifax Company for affordability platform by integrating the open banking service into its new Intelligent affordability service. With the help of the services, Digilytics AI can perform creditworthiness and mortgage affordability checks on its artificial intelligence dashboard and provide real-time insights to the lender.
With innovations and technological advancements evolving every day, relying on traditional methods in loan application checks needs a revamp. AI and ML have made considerable strides in the SME loan origination process by delivering efficient results. Numerous studies and statistics also prove this fact. AI models and products offer an accurate, fast, robust, and reliable loan origination for all SMEs.
About DigilyticsTM RevEl
Digilytics RevEl is a product that uses AI, ML, and NLP techniques to automate lending and deliver frictionless secure digital origination, increasing gross lending and productivity and reducing operations costs.
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