What data analytics areas need to be covered in the lending business

In order to make data-driven decisions in the lending business, a variety of data analytics techniques need to be employed. The resulting insights from data analytics should lead to supporting decision-making.

In a fast-paced business such as BNPL and point-of-sale consumer lending, there are many areas of decision-making. Many of those areas can be automated and decision models do not have to be executed by humans, but by a decision engine.

The areas that a lending business should cover with data collection and analytics include but are not limited to:

Marketing: Identifying and segmenting the customer base
Sales: Predicting customer behavior
Risk management: Using data to identify and assess risk

Credit policy: Assessing creditworthiness
Antifraud: Detecting fraud

Portfolio management: Forecasting loan performance. Predicting default rates and loss given default.
Product: Optimizing pricing and marketing strategies. Determining optimal interest rates.
Process management: Identifying opportunities for process improvement. Minimizing processing time
Market Insights: Benchmarking against industry peers
Customer services: analyzing customer satisfaction levels.
Employee performance: Analyzing employee productivity and motivation.

Each of these areas requires different data sets and analytical methods, but all are essential for making informed decisions in the lending business.

Marketing

Using data analytics to identify and segment the customer and marketing channels are most of the important tasks of the marketing data analytics team.

The tasks in marketing can be related to which marketing channels attract the best customers both from cost per lead, but also considering approval rate and risk performance of each channel.

The goal of marketing data analytics is to consider the profitability of each customer and also to find ways to increase the number of high-quality leads and loan applications.

Sales

In the sales area, data analytics is used to better understand what customers want and need.

This helps the sales team to close more deals by having the right products and services that meet customer needs. Additionally, data analytics can help the sales team to better understand customer behavior so they can define and prepare fitting products and partnerships.

In a business model, where sales agents are used, data analytics related to the sales performance of each agent and the performance of booked loans can help identify the best-performing agents.

Also, data analytics can help with designing the best incentive package for each agent.

Risk management

Risk management is one of the most important areas for data analytics in the lending business. This is because data analytics can help to identify and assess risk in the portfolio. It can also help to develop and stress test risk models.

In the risk management area, data analytics is used to better identify, quantify and manage risk.

This includes understanding which products and customers are the riskiest, setting limits on exposure to risk, and monitoring risk on an ongoing basis.

Additionally, data analytics can be used to monitor and report on risk on an ongoing basis. This is essential for keeping the portfolio healthy and avoiding losses.

Credit policy

Part of risk management is defining the credit policy for underwriting loans. Data analytics plays a role in this by helping to assess the creditworthiness of borrowers.

This includes understanding the financial situation of the borrower, their ability to repay the loan, and their credit history. Additionally, data analytics can help to identify trends in creditworthiness. The trends can be connected to the economic cycle or local economic situations.

Assessing not only the borrower, but the overall situation is important for setting the right interest rates and terms for loans. Additionally, it helps to identify which borrowers are likely to default on their loan

Antifraud

In the lending business, data analytics can also help to detect fraud. This is done by looking for anomalies in the data that can indicate fraud.

This includes looking at things like multiple applications from the same IP address or fake identities. Detection usually happens by identifying anomalies and sudden spikes in concentrations around a pivoting fact. Additionally, data analytics can help to identify patterns of fraud so that new cases can be preven

Outcomes of antifraud data analytics usually lead to changes in underwriting processes and changes to credit policy decision models.

Since these changes can happen often and require fast response, having a decision engine that is easy to operate is crucial in stopping fraud in its infancy. The longer it takes to implement new rules into policy, the larger can be the financial loss caused by fraud.

Portfolio management

Data analytics also plays a role in portfolio management. This is because data analytics can help to predict loan performance. Additionally, data analytics can help to identify which loans are at risk of default and which loans are likely to perform well.

This helps portfolio managers to make informed decisions about which loans to keep on the books and which to sell. Additionally, data analytics can help portfolio managers to forecast loss given default. This is important for setting provisions and for managing the overall risk in the portfolio.

Product

Data analytics is also used in the product area. This is because data analytics can help to optimize pricing and marketing strategies. Additionally, data analytics can help to determine the optimal interest rate for products.

This is important for making sure that products are priced correctly and for ensuring that products are marketed to the right audience. Additionally, data analytics can help to identify which products are most profitable and which are most at risk of default.

Considering these metrics, the profitability of a product is calculated based on considering the costs:

acquisition costs
servicing costs,
funding costs (interest expense)
costs of loss given default
collections costs

Against income streams:

interest income
income from additional services (fees, insurance)
late payment fees
potential future income from cross-sell and upselling activities.
sales comission fees from merchants

Process management

Data analytics can also help to improve processes. This is done by identifying opportunities for process improvement. Additionally, data analytics can help to minimize processing time.

This is important for making sure that loans are processed quickly and efficiently. Loans that are processed quickly attract better customers, who can choose their lender. More good customers lead to a better overall portfolio.

Market Insights

Data analytics can also help to provide market insights. This is done by benchmarking against industry peers. Additionally, data analytics can help to identify trends in the industry.

This is important for keeping up with industry changes and for making sure that the lending business is positioned correctly in the market. Additionally, it helps to ensure that the products and services that the lending business offers are aligned with customer needs.

Customer care

Data analytics can also help to optimize customer care. This is done by identifying customer needs and by identifying the best time to contact customers. Additionally, data analytics can help to identify upsell and cross-sell opportunities.

This is important for making sure that customers are happy and for ensuring that the lending business is maximizing revenue from each customer. It also helps to ensure that the customer care team is efficient and effective.

Analytics in customer care can also prevent unhappy customers to go to a competition for refinancing loans. A refinanced loan can lead to a loss of future income. If that happens on a larger scale of a portfolio, it can seriously harm the profitability of the overall lending business.

Employee performance

Data analytics can also help to optimize employee performance, especially in sales. This is done by identifying which employees are the most productive and by identifying which employees need additional training. Additionally, data analytics can help to identify which employees are at risk of turnover.

This is important for making sure that the lending business is efficient and for ensuring that employees are engaged and motivated. Additionally, it helps to ensure that the lending business is attracting and retaining the best employees.

Conclusion

In today's market environment, data analytics is being used more and more in the lending business. This is because data analytics can help to improve decision-making, optimize pricing and marketing strategies, and to improve customer care.

Additionally, data analytics can help minimize processing time, detect fraud, improve portfolio management, and provide market insights. Considering all these applications, it is clear that data analytics is becoming a critical part of the lending business.