A wide array of industries has already engaged in some kind of predictive analytics – numerical analysis of debt collection is relatively a recent addition. Financial analysts are now found harnessing the power of predictive analytics to cull better results out for their clients, and measure the effectiveness of their strategies and collections.
Let’s see how predictive analytics is used in debt collection process:
Since the late 1980’s, FICO score is regarded as the golden standard for determining creditworthiness and loan application. But, however, machine learning, particularly predictive analytics can replace it, and develop an encompassing portrait of a client, taking into effect more than his mere credit history and present debts. It can also include his social media feeds and spending trajectory.
The survival models evaluate each client’s probability of becoming a potential loss. If the account shows a continuous downward trend, then it should be regarded soon as a potential risk. Predictive analytics can help identify spending patterns, indicating the struggles of each client. A system can be developed which self-triggers whenever any unwanted pattern transpires. It could ask the client if they need any help or if they are going through a financial distress, so that it can help before the situation turns beyond repairs.
Businesses are keen to know about future cash flows – what they can expect! Financial institutions are no different. Predictive analytics helps in making more appropriate predictions, especially when it comes to receivables.
Debt collector’s business models are subject to the ability to forecast the success of collection operations, and ascertaining results at the end of each month, before the billing cycle initiates. As a result, the workforce of the company is able to shift their focus from the potential payers to those who would not be able to meet their obligations. This shift in focus helps!
Predictive analytics weave wonders; not only it has the ability to point which clients are the highest risks for your company, but also predict the best time to contact them to reap maximum results. What you need to do is just visit the logs of past conversations.
Last, but not the least, all big data models face a common challenge – data cleaning. As it’s a process of wastage in and out, before starting with prediction, company should deal with this problem at first to construct a pipeline, for feeding in the data, clean it and use it for neural network training.
In a concluding statement, predictive analytics is the best bet for debt and revenue collection – it boosts conversion rates at the right time with the right people. If you want to study more about predictive analytics, and its varying uses in different segments of industry, enroll in R Predictive Modelling Certification training at DexLab Analytics. They provide superior knowledge-intensive training to interested individuals with added benefit of placement assistance. For more, visit their website.
The blog has been sourced from — dataconomy.com/2018/09/improving-debt-collection-with-predictive-models
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