Month: September 2020

The consumer lending business is centered on the notion of managing the risk of borrower default. Non-performing assets are one of the major challenge lenders face. In this current unprecedented situation with the pandemic, real-time monitoring of transactions in the loan portfolio will provide yet potent ammunition for lenders to keep the risk profile of the credit portfolio, reeling under the impact of the situation, under check. Inconsistent or unreliable approaches to credit analysis expose firms to unnecessary risks. Lenders must be able to assess the risk of default for each customer so that they can decide to whom the offer should be granted, or actions should be taken.

 Unfortunately, accurately estimating the credit risk of a borrower is the most challenging task many lenders face. Lenders need a comprehensive approach to non-performing asset management.

Technology intervention in the form of Early Warning Systems help financial institutions to intelligently map and monitor flow of funds and prevent frauds

 

Need for an early warning solution

A comprehensive early warning framework that included identifying the right customer segment, understanding the data landscape, formulating early warning triggers and creating a risk mitigation plan can help substantially reduce firm’s NPAs.

The lack of due diligence before and after loan disbursal is the single largest contributing factor to the frauds apart from defaults, economic slowdown, and lax lending practices. With the help of disruptive technologies such as artificial intelligence (AI), machine learning (ML) and streaming, data- lenders will be able to “detect suspicious transactions in loan accounts on a real-time/near real-time basis, which will positively contribute to the overall health of the credit portfolio.

 

An early warning solution can help firms:

  • Reduce the new NPA flows( and the resulting reduction of NPA in stocks)
  • Maximise the recovered value and reduce the exposure at defaults with timely alerts
  • Better utilize capital

 

Best foot forward

Today’s early warning systems are more effective in not just monitoring and detecting red flags, but also putting firm’s interests ahead of individual interests by measuring and monitoring risks, placing banks back in control of their data and decisions.

So, how do they work? Early Warning Systems rely on multiple data sources to measure and monitor risks. There are four distinct transformative components to the whole system of extracting actionable insights from disparate data:

  • Collating data from multiple touch points
  • Cleansing, validating, and restructuring data into valuable information
  • Algorithmic processing using next-generation technologies and data modelling to generate insightful early warning signals/alerts
  • Case management by channeling 

Early Warning Systems that mine and understand credit-related data – both transactional and external touch points – could have detected and flagged anomalies ahead of time. This gives firms an inkling of inadequate governance in their hierarchies, transactions and customer accounts, before it becomes too late or too difficult to launch a recovery.

Now not only are lenders and financial institutions taking an active interest in governance and monitoring, they are going the extra mile by ensuring that the system is functional and advising additional measures from a regulatory perspective.

To know more Contact Us 

Managing accounts and ensuring on-time repayment are the success mantra of every small business lending. But when it comes to maintaining consistent repayments, most of the time it is a nightmare to lenders. In the lending industry, this comes in the form of delinquency and, ultimately, charge-offs.

Delinquency begins when customers miss their first payment. After a period, the company is required to move the account from delinquency to charge-off status. Most lenders are faced with the fact that a certain percentage of debtors will never pay debts owed. This can inhibit their company’s ability to balance its books and budget for the future. Consequently, companies record the debts as unlikely to be paid and report them to credit reporting bureaus as charge-offs.

This is the model for many businesses, but collections can begin sooner, and account balances can be resolved more quickly to the benefit of everyone. So why can’t lenders plan for an early-stage collection process (pre-charge off) than post charge-off balances? 

 

Pro active strategies to quell charge-offs before it starts

Pre-charged off accounts can involve collecting payments on accounts 1 or 2 days past due. The longer these accounts go unpaid, the longer they harm the creditor’s bottom line and the longer they can accrue interest, late fees, and negative credit reporting for consumers. All customers are not ready to be placed over to third-party collections, and therefore, with early stage collection approach works personally with every customer, reminding them of their obligation to bring their accounts up to date, while concretely maintaining your valued relationships. By using digital methods, proprietary segmentation, structuring, and analytic models, creditors can determine the best method of intervention and best agent to deal with and deploy the collection strategy on every specific case.

Early intervention has many benefits including:

  • Increased cash flow
  • Shorter payment periods 
  • Drastic Reduction in accounts that enter delinquency status
  • Reduced write-offs
  • customer master record updates
  • 24/7 online accounting tracking, monitoring and status updates
  • Complete payment processing options and payment updates (direct deposit, check-by-phone, credit card payments, e-payments, money orders or certified checks)

Being chased by debt collection agencies does not improve the situation. Digital Debt Collection enables consumers to self-manage payments and resolves their debts on their own before agents call.

 

Digital collection-a strategic priority for lenders

Effective risk prediction: Artificial Intelligence (AI) makes risk prediction accurate by analyzing rich data sources and increasing the accuracy of prediction models using machine learning (ML) algorithms.

Customized payment plans: With advanced digital and mobile technologies, mass personalization in debt collection is possible. A customized outreach and collection strategy can help collectors recoup their losses (some, if not all), while also helping customers pay off more of their debt. Personalization of debt collection strategy results in significant benefits – less outstanding debt, higher customer satisfaction and stronger relationships.

ML and Natural Language Processing Technologies : Businesses can aggregate customer data and proactively reach out to customers with alternative debt repayment options and credit counselling in order to mitigate their loss as well as prevent the customer from becoming delinquent

Effective debt recovery plans: AI powered chat bots, by analyzing financial history, can communicate with most likely to default customers, on their preferred channel, at the time convenient to them. Through videos or online content, bots can deliver relevant debt repayment options and recommendations.

There are several things that companies can find themselves with charged-off accounts to consider. First, they need a solid understanding of charge-offs. Second, they need to put in place a strategy for handling them.

To digitize your collection process and to improve resilience against credit losses, Talk to Us

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