From Default to Debt: Stemming the Flow with Bad Debt Analytics

Telecommunications customer service teams have long been tasked with multiple roles – delivering superior experiences, being savvy with multiple tools, working with bots or assisted automatons, and curating omni-channel interactions to name a few. They are also responsible for ensuring that customers complete payments and clear their dues on time.

While many solutions are available to enhance the operational efficiency of contact centers, this particular role – managing bad debt – requires a different approach, and here’s why. Globally, the incidents of fraud and defaulters are increasing, which put customer service teams on the backfoot as they struggle to make finance-related decisions. like:

When late payments morph into bad debts

The words ‘bad debt’ began as a way for banks and credit card companies to refer to customers who defaulted on their loan or credit card payments. Today, the telecommunications industry borrows this term from finance for postpaid and enterprise customers who have not cleared their dues as per their billing cycle. Bad debt is a real challenge within the telecommunications industry and a contributor to annual revenue losses. Bad debts can be as high as 2% of the total revenue for telecom operators.

Billing to collection lifecycles in telecom are a bit different from the banking and lending industry. Banking and credit firms often deal with chronic defaulters through one-time settlements or loan restructuring. Telecommunication players, on the other hand, have a different play. Since initial debt recovery falls within the realm of customer service teams, the agents execute gradual actions like sending recurring payment reminders and barring outgoing calls. They often have no way of knowing whether a late payment is a genuine one or whether it is nefarious, leaning towards non-payment. Finally, as the default risk becomes severe, the job is outsourced to an external vendor – a third party collection agency – whose job it is to recover the payment or ‘bad debt’.

The question is: Is there a way to predict the risk of bad debt way ahead of time?

Here too, telecom can take a leaf out of the lessons and examples demonstrated by finance. Many global banks and lending agencies are investing in ways to capture 360-degree customer data in a bid to create large datasets that feed intelligent risk scoring models. Such models use atypical customer data to forecast the likelihood of credit risk and credit fraud. These predictions are then leveraged to make smarter decisions about lending, like levying higher interest rates, thereby bringing down the rates of default.

Bad debt analytics

An analysis of the data collected during each stage of billing to collections lifecycle will yield key insights. The five main stages are:

Stage 1: Bill generation

Stage 2: Pay-by-date

Stage 3: Early delinquency stage (time of default > 30 days)

Stage 4: Advanced delinquency or bad debt (time of default is between 60-120 days)

Stage 5: Collection, disconnection or legal action (time of default > 120 days)

Analytics models can ingest this lifecycle data to reveal patterns and correlations that provide a probability score of defaulting to bad debt. This entire process is known as bad debt analytics. It focuses on, among other things, minimizing how many customers move from Stage 2 to Stage 3. As seen in the image, stage 3 is the critical stage that separates late payments from defaulters.

Tools to facilitate early detection of bad debt

Bad debt analytics are a constellation of modern tools and technologies that can greatly assist customer service teams in making the right decision at the right time when late payment instances arise. It can also provide a host of correlations that inform onboarding about the propensity to default. Some of these tools:

  • Automated data capture – Telecom databases are replete with voluminous customer information that contain key insights. But first, this data lying in disparate systems across sales, marketing, finance, and network teams must be integrated into a single repository. Through automated data capture using connectors and APIs, Telcos can unlock data siloes, mechanizing the job of real-time data integration. This integrated data is what feeds bad debt analytics models for relevant output.
  • Machine learning models – These models accelerate the data-to-insight lifecycle by crunching data, understanding trends, and finding patterns that cannot be perceived by humans. An example is how one of our models uncovered that a majority of the defaulters in the handset sales department of a leading Middle East operator were migrant customers. This insight helped the telco adapt its credit processes to promote appropriate repayment behaviors and lower its bad debt risk.
  • Smart dashboards – Output from the models must be displayed in the right manner to the customer service teams so they can take or recommend the right actions at the right time. For instance, high risk of default in a specific location may indicate poor KYC practices by salespeople. Knowing this, the operator can immediately halt new sales transactions in that area and redirect staff for training.

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