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Transforming Lending with Predictive Insights

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Transforming Lending with Predictive Insights

February 8, 2016 | Published in CPI Financial

Deteriorating credit quality: Ringing the bell for change?

Banks play a key role in facilitating Africa’s economic growth. To support this critical function, they are increasingly looking at technology to play a vital role by helping lenders make faster and more accurate credit decisions. According to the IMF, identifying sufficient lending opportunities has been a key problem for African banks. This has, in turn, led to excess liquidity within the banks with the ratio of liquid assets to total assets exceeding 25 percent. In addition to that, non-performing loans constitute 7.8% of the total assets for banks in the middle income countries in Africa as compared to 1.9% for the rest of the world. The banks are seeking to not only attract and retain the right customers, but also to make their collection processes more effective and improve their overall credit quality. While they are wary of turning down potential clients which reduces their profits and may also damage the bank’s reputation, they are equally concerned about making the wrong decision - accepting business that will result in future Non Performing Loans.

The rate of change of technology, competitive offerings and economic conditions makes matters more challenging. While improving their reach and retaining existing customers, lenders need to improve their operations across the entire loan lifecycle. How do they do that? Operating on real time data rather than historical data would be a good place to start.

Transforming Lending with Predictive Insights

Lending decisions have been proven to be more effective when they are made based on predictive insights powered by real time data. With the exponential growth and availability of data, both structured and unstructured, big data can be combined with historical, transactional data to uncover new opportunities and bring down costs. For example, by leveraging big data at the underwriter decision making stage, decisions for refer/on hold applications can be made after analyzing the current behavioral and risk patterns of the customer. The number of investigations for on hold applications is reduced thereby bringing down time and cost while also freeing people to focus on more important activities. In addition, fraudulent customers can be detected more easily.

Risk managers across the globe are looking to use structured and unstructured data to make more accurate risk predictions along with improving their understanding of the potential impact of a range of risks. They also seek to link them better to the organization’s strategy. Customer Relationship managers are actively looking at analytics to improve customer service while the collection departments want to use analytics to drive down costs and at the same time increase collections. In fact, every major decision in a bank to drive revenue, control costs or mitigate risks in their lending process can be improved by using real time data and analytics.

Financial institutions worldwide are increasingly leveraging analytics to

  • Reach the right customers with the right products
  • Deliver a superior customer experience through faster on-boarding
  • Identify, target and retain the most profitable customers
  • Drive up recovery rates while driving down collection costs

Financial institutions can gain a competitive edge with the ability to make data-driven decisions seamlessly throughout the lending value chain. In a recent survey of global financial institutions, 55% of the respondents said they were using predictive analytics to create new revenue opportunities, 45% said they were leveraging predictive analytics for customer services and 43% of respondents were using the results of predictive analytics for product recommendations and offers.

So how do financial institutions leverage analytics to deliver value at each stage of Customer and Loan Lifecycle?

  1. Meet Mark, Dave and Caroline. Three different people with different preferences, different banking needs, different credit histories, and different purchasing patterns. Mark is looking for a personal loan while Caroline has a housing loan requirement. Dave does not have a loan requirement currently. The bank wants to reach the right customers with the right products.

Financial institutions can reach the right customers at a lower cost with accurate customer segmentation which helps improve targeted marketing efforts. Marketing campaigns can be launched through the right channels for improved reach and enhanced business impact. Historical multi-channel customer data can also be analyzed to offer best-fit products for each segment based on customer propensity.

  1. After applying for loans, Mark and Caroline come to the bank to submit their documents. The bank wants to ensure a fast and hassle-free onboarding process for them.

By automating complex tasks, analytics can help reduce the loan origination time and support faster credit decisions based on the segment’s risk profile. The loan processing time is reduced through auto credit decisions driven by scorecards and strategy maps resulting in enhanced customer experience. This results in the bank significantly improving the customer acquisition process and the loan book quality.

  1. Mark is now a highly valued customer of the bank. The bank is happy and wants to make him a customer for life

CRM data and loan repayment patterns are analyzed enabling lenders to identify the causes of customer churn, and gain insights as to how to retain profitable customers. Relevant products can then be bundled to increase customer lifetime value and portfolio diversification.

  1. Caroline has, however, defaulted on her loan repayment. The bank wants to recover the overdue amount as cost effectively as possible, ideally while creating the minimum upset.

Predictive insights help the bank develop optimum collection strategies across all the stages of loan delinquency, reducing operational expenditures and increasing recovery rates. They can be used to customize the strategy for each client profile. Collection activities are intensified based upon the type and level of client relationship, as well as the probability of debt recovery. Improved predictions result in a significant decrease in non-performing loans.


The Way Ahead: Analytics to play a pivotal role in driving efficiencies in the Lending Industry

While some financial institutions have begun to see real benefits of using real-time data to gain insights, many are still working in isolated silos and hence are not able to leverage predictive analytics at the optimal level. However there is a gradual shift in the mindset with more and more financial institutions looking at analytics to improve their loan lifecycle management. With the huge growth of data, financial institutions can gain a strategic advantage by using predictive insights to make improved lending decisions that are better aligned to current and future economic conditions. They can use predictive analytics to rapidly adjust to fast changing market conditions and steer their portfolios through uncertain times.