Digital Lending

Effective risk management is crucial for lenders as it helps them identify, evaluate and mitigate risks. The rapid growth of credit operations, rising customer expectations for personalised solutions and lower turnaround further emphasise the need for robust risk assessment and management tools.

As per a report in Hindu Business Line, retail loans grew at a compounded rate of 24.8% for the year ending March 2023. This is almost double the growth rate of advances for the year ending March 2022.

In this scenario, automated lending systems help lenders meet customer expectations, offer them personalised solutions and manage the rapidly increasing loan applications more effectively. Furthermore, these tools serve as a valuable asset for risk management and eliminate the problems associated with manual risk assessment.

The blog post focuses on 10 ways automated lending systems help lenders mitigate risk.

10 Ways Automation Mitigates Risk for Lenders

Traditionally, lenders carried out credit appraisal and risk assessments manually. This involves extensive paperwork and dependence on obsolete credit scoring models which rely on subjective rather than objective, data-based inputs. These risk management models often lead to inefficiencies, higher turnaround time, errors in judgement, and may also be biased.

Modern risk assessment tools that leverage Artificial Intelligence (AI) and Machine Learning( ML) do away with the problems associated with manual risk management and offer several advantages.

Here’s how lenders can gain in the following ways when they use an automated lending system:

1. Harness the power of data analytics 

Automated credit assessment systems can process large volumes of data much faster with better efficiency. Robust data analytics can spot patterns and anomalies that are not apparent to humans, resulting in better risk assessment and decision-making.

Intelligent solutions like Finezza’s Analytics suite provide results that improve and get richer and deeper with each usage.

2. Conduct more stringent credit checks

An automated lending system helps lenders scrutinise loan applications more thoroughly. Financial Institutions (FIs) constantly have to walk the fine line between enhancing their customer base and maintaining the quality of their portfolio. They do not want to deny credit to deserving applications and want to promote financial inclusion; at the same time, they want to minimise the default risk.

Filtering customers at the application stage ensures only qualified customers get loans. Loan automation systems evaluate more data points and sources, usually overlooked in manual processing to assess applicants. AI and ML-based risk scores for borrowers help identify potential default risks.

3. Real-time monitoring

Simultaneous tracking goes a long way in mitigating default risk. Credit health is a dynamic concept, as the creditworthiness of an individual may change with time. If the credit quality of the lender’s portfolio deteriorates due to defaults and missed payments, real-time monitoring can help them take timely action.

With an automated lending system, lending companies can track and monitor loans more effectively and act promptly with responses like sending frequent reminders. Real-time risk monitoring helps reduce bad debt risks with real-time alerts and credit reviews.

4. Eliminate human bias

Besides offering a more accurate evaluation of applicants’ repayment and risk capacity, advanced algorithms of automated lending systems help remove human biases from the process.

Automation of risk assessment guarantees uniform and unbiased evaluation for all applicants. This ensures that only worthy applicants get loans, and no deserving applicants are denied credit due to inherent biases.

5. Reduce Errors

Copious amounts of paperwork involved when processing a loan application in conjugation with the pressure of reducing the turnaround time, can result in errors.

Automation eliminates the risk of manual errors occurring while entering data and processing, which could lead to faulty decisions.

6. Utilise alternative data

Automation also helps FIs look at alternative data sources that traditional risk assessment tools and models usually overlook. Cross-product data from internal sources, social media inputs, online buying patterns, utility bill payment details, and more help provide a comprehensive customer profile and aid risk assessment.

7. Better compliance

The lending sector is heavily regulated; adhering to various compliance guidelines set by regulatory bodies is crucial for all FIs. Non-compliance with these guidelines can result in penalties and make the financial institutions susceptible to instability and fraud.

Automated lending systems ensure timely and accurate complaince. They also help lenders align their policies to the regulatory framework.

8. Predictive analytics

Predictive analytics helps FIs identify potential risks which are likely to occur. This analysis can help lenders take preventive measures and make more informed decisions.

The predictive analysis forecasts future outcomes considering current and emerging market trends and identifies anomalies that may go unnoticed by traditional methods.

9. Minimise Strategic Risk

Automation helps FIs align their underwriting decisions with their risk policy seamlessly. Any updation in the risk policy demands a change in the underwriting process too. Automated loan processing systems require a few tweaks to update the approval process.

Compare this to updating the manual risk assessment process, which would require extensive training and ensuring the updated information reaches each team member and they comprehend it well. This could be a time-consuming, expensive process, fraught with teething troubles.

10. Facilitates risk-based pricing

Automated lending systems facilitate pricing loans in line with the borrowers’s risk profile and ensure that lenders get compensated for the risk they are taking on.

Risk-based pricing allows lenders to offer different consumers different interest rates or variations in other terms, depending on the risk profile perceived by the system.

Risk-based pricing models also enable better risk management. For example, a customer who offers collateral could get loans at lower rates, just like a customer with a high credit score.

Conclusion

Risk is inherent to the lending process; automated lending systems help lenders mitigate these risks and enhance efficiency and customer satisfaction. Financial institutions can perform better credit checks and offer customers risk-based pricing by automating their lending process. As a result, automation helps lenders make intelligent decisions based on data, analytics, assessing alternative data points and predictive analysis, which reduces their risk.

If you are looking for a solution that offers all this and more, then Finezza has the right solution for you. Their automated loan process system is a cloud-based, AI-enabled solution that automates the entire loan processing workflow.

Finezza also offers an all-in-one credit evaluation solution that merges data reports from multiple sources with AI-powered predictive analysis to help lenders make risk-free decisions.

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The digital lending life cycle space in India is growing exponentially. The Research and Market Report revealed the Indian Digital Lending Platform Market is anticipated to grow at a CAGR of 27.95% and grow to $2507.55 million by 2027.

Artificial Intelligence (AI) and data analytics are helping lenders sustain and fuel this growth via a streamlined lending life cycle. While AI enables the automation of repetitive manual tasks, saving time and labour expenses, data analytics offers valuable insights and helps predict borrower behaviour and formulate strategies.

Financial institutions globally are embracing AI-based systems for the various benefits they offer, with over $217 billion spent on AI applications in 2021 alone to help them prevent fraud and assess risk.

In this blog, we explore how integrating AI and data analytics can help lenders transform the lending life cycle and offer numerous benefits.

Lending Life Cycle: An Overview

The lending life cycle involves multiple stages and stakeholders- lenders, borrowers, underwriters, credit bureaus, third-party service providers and regulators. Let’s elaborate on its key phases:

1. Pre-qualification

This phase sets in motion the loan origination process; the applicant inquires about the loan from the prospective lender and submits the required documents.

2. Loan Application

If the applicant qualifies for the pre-qualification stage, they submit the loan application, along with the documents, as informed by the lender.

3. Document Processing

The loan application is sent for processing to the credit department. The lender asks the applicant for additional details( if required) and verifies the documents.

4. Underwriting and Evaluation

This is a crucial phase in the lending lifecycle; the credit appraisal department assesses the applicant’s eligibility, creditworthiness and risk profile.

5. Loan Approval and Disbursement

Once the lenders are satisfied with the applicant’s credit health and the paperwork, they approve the application and disburse the loan.

6. Loan Repayment and Closure

This phase deals with tracking and monitoring the loan repayment process and taking suitable steps in case of delays in repayment or missed instalments. Monitoring loans closely after disbursement is integral to a lender’s success. After complete repayment, the loan is closed in the lender’s record.

How Artificial Intelligence and Data Analytics Facilitate Lending Life Cycle Transformation?

Technological innovations are transforming the entire financial landscape. Below are five ways AI and data analytics can positively impact the lending lifecycle.

1. Streamlines Loan Processing

AI can help financial institutions automate various steps in the lending phase, like data compilation, verification of documents, credit assessment and more.

Lenders can reduce costs and improve efficiency with workflow automation in the entire lending life cycle. Automation helps reduce processing time, minimises errors, and streamlines loan processing operations; optimising loan origination procedures with data analytics improves business performance.

Document identification and data extraction solutions help lenders accelerate the KYC process by quickly extracting information such as faces, names, numbers, addresses, transactions, and more in real time.

2. Enhanced Credit Evaluation

AI-powered machine learning (ML) models help lenders improve risk management by extracting relevant data, identifying trends from complex unstructured documents and correlating these data points meaningfully.

During the lending life cycle, large volumes of data processed with data analytics tools can help you accurately understand the applicant’s creditworthiness. It also helps lenders consider alternate parameters like online buyer behaviour, utility bill payment history, demographic aspects and more to get a more holistic borrower profile.

AI and data analytics can predict loan defaults by analysing borrower data, economic indicators and market trends, which helps lenders mitigate risks and optimise portfolio performance by

For example, Finezza’s Bank Statement Analyser is an AI-based tool that helps analyse borrowers’ bank statements. It determines financial patterns, assesses the borrower’s financial stability and documents authenticity.

3. Better Loan Monitoring

Lenders can manage risky accounts and potential defaults with the help of insights which AI-powered algorithms provide by analysing data from multiple sources. In the lending life cycle, real-time monitoring of loans helps lenders stay updated on any possible defaults and take appropriate action quickly.

Also, data analytics tools allow Lenders to gain a deeper and more nuanced understanding of their customers. Effective intervention for debt collection based on data sources like debtor demographics, account activity, and risk ratings can help reduce defaults.

For instance, the Collection Delinquency Management system helps automate and simplify loan collection for lenders with a sophisticated, customer-centric solution that leverages AI and data analytics.

4. Promotes Financial Inclusion

Some applicants may find it challenging to access credit due to biases inherent in the system due to manual credit appraisal and loan processing; their loan application may not cross the first stage of the lending life cycle.

AI and data-driven algorithms base their decisions on objective data and other intelligent inputs, not perceptions or human judgement. This allows deserving applicants to avail of credit and promote financial inclusion.

Traditionally, banks use credit scores to evaluate a potential borrower, which may leave out a large chunk of the population from the credit availability. Using alternate data to assess an applicant’s creditworthiness can help applicants with no credit history avail of credit.

Financial institutions can reduce the chance of bad loans, customise solutions and increase their customer base by employing AI and data analytics.

5. Improved Customer Service and Debt Recovery

In the lending life cycle, smart chatbots offer clients comprehensive and personalised self-help solutions with the help of AI. They can update borrowers on their loan status, closure and personalised solutions. Virtual assistants and chatbots can also guide borrowers through the loan application process and improve their chances of loan approvals.

Loan chatbots make debt recovery simpler by sending timely payment reminder messages. Real-time updates about EMIs improve the chance of borrowers repaying promptly. Chatbots also reduce the effort and time required by field agents for debt recovery.

As per a Times of India report, AI-powered voice bots have achieved 40% higher engagement rates, in some cases significantly reducing human calling costs and overall collections time.

Key Takeaways

Lenders can improve their operations, make better credit decisions and provide better customer service throughout the lending life cycle with the help of solutions that leverage data analytics and artificial intelligence.  Financial institutions can offer customised solutions, facilitate financial inclusion and increase their customer base by employing AI and data analytics.

Software solutions incorporating artificial intelligence and data analytics, like the suite of products offered by Finezza, can help lenders in each stage of the lending life cycle, benefitting lenders and customers alike.

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The digital lending industry has transformed the landscape of borrowing and lending money. Today, traditional banks are not the only option for financing. Digital lending solutions have evolved as potent catalysts for economic growth and financial inclusion, predominantly in emerging markets. Experts predict that the digital lending sector will reach a massive USD 1.3 trillion by 2030

Using data analytics, mobile connectivity, and digitalisation, these innovative solutions transform the way people in need are given loans. Moreover, through the digital lending solution for the unbanked, the gap between the formal financial system and the underserved population has considerably reduced. This has benefited a large number of people as well as small businesses.

What is Digital Lending Solution?

Digital lending means offering loans/credit facilities using digital technologies and innovative online platforms and evaluating borrowers’ creditworthiness of the borrowers using data analytics. With any internet-capable device, and irrespective of location, the digital lending solution enables borrowers to apply for loans – be it consumer loans or commercial loans.

Financial institutions or banks can enhance their efficacy and loan income/revenues through digital lending while delivering quicker services at the point of sale (POS). Since all documents and transactions take place online, digital lending removes the need for physical paperwork. This has led to convenience and a lesser likelihood of fraud and errors.

Moreover, digital lending platforms offer personalised loan products and interest rates based on borrowers’ needs and credit profiles. This has resulted in more attractive loan offers and an improved borrowing experience.

Role of Digital Lending Solutions in Emerging Markets

Fuelled by favourable socioeconomic factors, digital lending has risen exponentially in recent years. Speeder credit approval and addressing prevailing credit-related challenges have given digital lending significant advantages and an edge over traditional lending in emerging markets.

Moreover, the application of innovative technologies, and the digitalisation initiatives by governments of various nations, have given a fillip for enhanced digital financial infrastructure. These factors have driven the lending revolution in emerging markets.

The role played by digital lending  is as follows:

1. Financial Inclusion

World Bank’s Global Findex database showed that, globally, approximately 1.4 billion adults are unbanked. Most of the unbanked live in emerging markets, lacking access to crucial financial services owing to limited traditional banking infrastructure and obstacles to financial inclusion.

However, the rapid progress of digital lending alleviated the disparity and challenges related to financial inclusion. Digital lending platforms evaluate borrowers’ creditworthiness using cutting-edge technologies such as machine learning (ML), big data analytics (alternative data sources (borrowers’ payment histories/social media behaviour/mobile phone usage)) and artificial intelligence (AI), going beyond the limitations of traditional credit rating techniques.

Hence, this has enabled people with no formal credit histories to build a credit profile and avail of loans customised to their specific needs. Moreover, easier access to credit facilities empowers the underserved population to partake in different economic activities and have better financial futures.

2. Faster and Efficient Credit Access

The efficacy of digital lending solutions is unmatched. The digital lending solution platform allows borrowers to apply for loans online and submit all documents online. This speeds up the approval process, in most cases, the approvals being granted within a few hours.

This is unlike the traditional lending process, which encompasses cumbersome paperwork, and borrowers needing to submit various documents in person. Hence, this leads to delays and prolonged waiting periods.

In the majority of emerging nations/markets, small- and medium-sized businesses are key drivers of economic progress. Timely and speedier financing enables these businesses to expand their operations and create employment opportunities, thus contributing to the nation’s economic growth.

3. Financial Innovation

The digital lending environment acts as a hub for financial innovations. The digital lending solution platforms partner with various fintech and financial service providers to offer an assortment of products such as insurance, investment opportunities, and digital wallets.

This combination of services and products builds a holistic financial ecosystem, catering to diverse groups and needs, further improving financial inclusion. Digital wallets help users (who do not have a proper bank account) in storing, transferring, and managing money effectively. Hence, such individuals benefit from better financial management options.

In addition, the wide range of products available on digital lending platforms encourages individuals to use digital mediums for all financial transactions. Increased digital adoption accelerates the digital transformation pace in the lending/finance sector in emerging markets resulting in cost-effectiveness and great efficacy.

4. Scalability and Reach

Conventional lending institutions generally face many obstacles in terms of geographical reach in emerging markets. Being online, the digital lending solution can be accessible using mobile phones, hence scaling quickly and reaching rural/remote areas. This enables to extend financial/lending services to the underserved and unbanked population.

As previously mentioned, digital lending solution platforms are pioneers of innovations, especially credit-scoring innovation. These platforms constantly enhance their algorithms with the incorporation of new machine-learning methods and data sources. Hence credit evaluations of prospective borrowers (especially the unbanked/underserved) are more accurate and inclusive.

Furthermore, in emerging markets, informal sector economic activities can be formalised through digital lending solutions. Most of the businesses in the informal sector struggle to get credit facilities.

However, digital lending platforms offer loan facilities to businesses in the informal sector. To evaluate the borrowers’ creditworthiness effectively, these platforms adopt sophisticated risk assessment methods, thus reducing the probable risks related to informal sector lending.

Providing loans to the informal sector enables formalisation (requiring borrowers to register their businesses, maintain proper financial records, comply with regulatory requirements, etc.) and foster transparency. This leads to the growth of the informal sector, thereby contributing to economic development, employment opportunities,  and inclusion.

Conclusion

Digital lending solutions have reshaped emerging markets through the promotion of financial inclusion, aiding small- and medium-sized businesses, and empowering individuals. The digital lending solution platforms provide cost-effective and speedy financing by effectively using advanced technologies and data analytics.

Nevertheless, care must be taken to ensure issues such as data privacy and regulatory compliance are addressed. Partnering with banking institutions, regulators, and fintech firms will ensure inclusive and ethical practices. This will help to make full use of digital lending potentials, augmenting the development and progress of emerging markets.

Finezza is an all-in-one Lending Lifecycle Management Platform and Credit Evaluation Services platform that simplifies lending management and enables efficient loan handling and credit assessment. It automates loan origination, underwriting, and servicing, offering advanced credit evaluation tools.

With Finezza, financial institutions experience faster loan approvals, improved risk management, and enhanced customer satisfaction. This comprehensive platform streamlines the lending process, empowering financial institutions to optimise operations and provide efficient lending services.

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The global digital lending market is expected to grow exponentially in the coming years. The increasing demand for financial services and the rising adoption of digital solutions in traditional banking and lending services drive this growth.

Data Bridge Market Research shows that the digital lending market is expected to reach USD 69937 million by 2029, at a CAGR of 19.4% from 2022 to 2029.

The benefits provided by digital lending platforms, such as improved loan optimisation, faster decision-making, compliance with regulations and rules, and improved business efficiency, are expected to drive market growth.

But along with the growth, there are various challenges that digital lenders must address to ensure that their operations are compliant with Anti-Money Laundering (AML) and Know Your Customer (KYC) regulations.

The article explains the importance of KYC and AML compliance management for digital lenders, and we learn how this can be achieved.

What Are AML and KYC and Their Use in Digital Lending?

AML is a set of procedures, laws, and regulations designed to detect, prevent, and report suspicious financial activity. KYC is a process that requires financial institutions to verify the identity and background of customers before onboarding them.

Digital lenders use AML and KYC to protect themselves and their customers, minimising financial crime risk. For example:

• AML helps to detect and prevent money laundering and other financial crimes by identifying suspicious activity, such as large or suspicious transactions, and tracking the source of funds.
• KYC helps to verify the identity of customers, ensuring that they are who they say they are and that they are not involved in any illegal activities.

Together, AML and KYC help to protect the lender and the customer, ensuring that the customer is legitimate and the funds are being used for legitimate purposes.

5 Major Challenges in Managing ALM and KYC

AML and KYC processes are crucial to ensure compliance with anti-money laundering directives. However, many digital lending companies inadequately use their time and resources, creating ineffective AML and KYC processes.

Some common challenges they face are listed below:

1. High Cost of Compliance

Digital lenders must invest significant resources to comply with AML and KYC regulations. This includes hiring compliance personnel, maintaining necessary technological infrastructure, and investing in third-party services.

2. Complex Regulatory Requirements

AML and KYC regulations are constantly changing, and digital lenders must stay up to date with the latest requirements. It can be a difficult task, especially for smaller digital lenders.

3. Data Protection

Another critical requirement for digital lenders is securely storing and processing data. This is especially important regarding KYC and anti-money laundering data, which are highly sensitive.

4. Lack of Resources

Many digital lenders lack the resources to manage AML and KYC compliance properly. It may lead to inadequate compliance and possible penalties for non-compliance.

5. Fraud Risk

Digital lenders must take measures to protect against fraud and money laundering. This requires a comprehensive AML and KYC compliance programme that can detect and prevent suspicious activities.

7 Ways to Comply With AML and KYC Regulations as a Digital Lender

AML and KYC regulations protect against financial crime, including money laundering, terrorist financing, and other financial fraud. To prevent financial crime from occurring, digital lenders need to ensure their processes are compliant with these regulations.

They can consider several factors, including the following:

1. Risk-based approach

They must adopt a risk-based approach to customer onboarding and ongoing monitoring. This includes conducting identity verification, customer risk profiling, and transaction monitoring.

Identity verification ensures that the customer is who they say they are, while customer risk profiling assesses the customer’s risk profile.

2. Employing AI and ML

Digital lenders should also employ advanced technologies like artificial intelligence (AI) and machine learning (ML). AI and ML can help automate customer onboarding and ongoing monitoring processes, making them more efficient and accurate.

3. Focus on Data Security

Digital lenders must also ensure that their data security measures are up to date. This includes using secure, encrypted communication channels and robust authentication methods like biometrics.

4. Transaction monitoring

Digital lenders must monitor their customers’ transactions to detect any potential money laundering activity, which may include suspicious transaction patterns or transactions that involve high-risk countries so that they can detect potential money laundering activities.

5. Customer due diligence

A digital lenders should ensure they perform appropriate diligence on their customers to accurately verify their identities and have evidence of beneficial ownership of their accounts. High-risk customers should have enhanced due diligence measures applied to them as a result of these diligence measures.

6. Utilise Advanced Analytics

By analysing customer data, advanced analytics can help digital lenders detect fraudulent activity and money laundering. These analytics can also help digital lenders better understand customer behaviour and predict future risks accurately.

7. Monitor Changes in Regulations

Digital lenders need to stay up-to-date with changing KYC and AML regulations. This can help ensure their processes always comply with the latest regulatory guidelines.

Final thoughts

In 2023, Digital lenders must follow these steps to provide complete security to their customers. It will mitigate financial crime risks and ensure its operations comply with AML and KYC regulations.

A fintech software that automates the loan origination process ensuring any fraudulent activity is detected early can help in thorough compliance.

We at Finezza offer a service that can help you accelerate the time it takes to process KYC documents, which enables easy extraction of information such as faces, names, numbers, addresses, transactions, and more from images of your KYC documents in real-time.

As a provider of document identification and data extraction solutions, we help to facilitate the tagging of all data points and the automatic validation of the documents that are submitted.

We also offer a loan management system that helps our customers with the automatic verification of documents and easy integration with APIs.

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