SCORE Blog

90% of data was produced in the last two years alone, and we’re producing 2.5 quintillion bytes of data per day if DOMO’s landmark study on big data1 and IBM2 are to be believed. Generated from online transactions, web searches, social media posts, digital photos or video and GPS signals, to name a few,  big data is changing the world’s debt collection landscape, offering opportunities to leverage analytics and internet and mobile connectivity to improve margins and flag trends that indicate the need for new strategic direction. With the rise of digital collections, debt collection agencies (DCAs) who fail to adopt a nimble and robust collections strategy risk losing competitive advantage. Below are just some of the ways that digital technology can improve the collections process.

 

 

Digital collections platforms allow ease of use by embedding self-service into the collections process. For example, a customer should be able to go directly from an email or SMS reminder to the payment portal. Deloitte3 references cutting-edge two-way SMS technology that utilizes artificial intelligence. Customers can text the collections agency and receive feedback and advice from intelligent bots. The software can mine data from social media, payment history, financial and insurance records, smartphone activity, IoT devices and more to create an accurate customer profile that allows them to personalize debt repayment options according to the customer’s preferred channels4.

 

Once the customer accepts a payment plan that meets policy and compliance requirements, it can immediately be applied to the customer’s debit account – without the need for underwriters, call centre agents or additional verification. This automated self-service approach can target customers who are embarrassed by their debt and prefer not to talk about it, or those who frequently forget to pay. It can improve outreach and collection, resulting in better customer relationships. Customers can also quickly alert the agency in cases of fraud or bankruptcy, saving DCAs time and money.

 

 

Intuitive dashboards that display a single customer view and all relevant, updated and verified information pertaining to that customer certainly help collections agents become more efficient, allowing better customer relationships and outcomes.  This is critical, as a missing signature, date or full first name can make a customer’s debt unenforceable5. Additionally, the same technology can be seamlessly applied across collections functions, for example, filling in missing information by aggregating data from several sources, creating charts in Excel, formatting PowerPoint slides or modeling customer behavior.

 

 

Predictive behavioral models, which analyze rich data sources beyond traditional ones and increase accuracy through machine learning algorithms can provide previously unimagined insights, such as knowing when the best time to contact someone and what platform to reach them on. Predictive analytics can leverage alternative sources of personal data such as transactional data, payment information as well as social and online activities to determine financial behaviour and thus, the probability of debt repayment. For example, a customer who falls behind on payments due to a recent job loss will have corroborating data on social media. DCAs can proactively improve on debt collection by leveraging this data to offer alternative debt payment options and credit counselling. 

 

 

Top notch IT platforms are critical for aggregating data, organizing workflows and processing payments, but the core platform must be adaptable to take integration, flexibility and speed into account. Where different systems and feeds are integrated, debt collection agencies become more efficient and eliminate tasks being repeated, such as calling a customer twice in one day to remind them about an outstanding debt. DCAs with core IT that can adapt to non-standard data fields (such as student loans or non-prime lending) can identify the most profitable segments and quickly branch out into new directions.

 

Debt collections agencies that can translate data into effective strategy can reduce risk and increase recovery. If a database is updated with new information, such as a customer purchasing a car or paying off their personal loan6, this could suggest it is time to get back in touch.  Some firms use pay-day lending data to flag signs of stress within credit risk models. By exploiting analytics, DCAs can know the estimated costs of collecting debts and servicing costs and develop collection strategies accordingly. Analytics can also help you assign staff to customers whose behavior is most likely to be influenced by email, phone or written reminders and tailor multi-channel communication strategies to consumer preferences.

 

 

An automated and data-driven, centralized system ensures compliance through set procedures and communications that can be structured to consumer preferences. Advanced verification allows communication with the right person and provides an audit trail. DCAs must ensure compliance with data privacy laws and regulatory best practice, for instance, the use of personal data in collection. Workflow automation that incorporates risk governance and organizational design can define roles to optimize quality, adaptability to changing requirements, implementation and governance. Capacity and resource modeling can also provide critical information on the impact of collections decisions on other areas of the business, thereby improving allocation of accounts.

 

 

DCAs benefit from visualizing their end to end process through a visual workflow map that records all decision points and criteria in order to convert strategy into codes of conduct. For example, during early collections, segmentation allows identification of forgetful customers. The next step may be to send out a reminder SMS using specific, pre-approved wording. For other segments, different process steps, scripts and treatment paths will be followed7. Seeing the process can help when designing controls, demonstrating compliance, generating missing information and providing quality assurance. While this software has been around for a few years now, user interfaces have become more intuitive, allowing users to click and move components to reprogram the underlying decision engines. This helps firms quickly amend processes to reflect new regulations or try out new strategies. The number of accounts in different process routes can be tracked and reported and QA teams can conduct risk-based assessments. Superior analytics, more effective collections platforms and increased productivity from a better user interface and improved customer experiences will see the costs to collect decreasing and a lowering in the cost of demonstrating compliance. Ultimately, the collection environment has become incredibly dynamic, and the companies that have most advanced systems will perform better than those without.  SCORE leverages technology to make the debt collection process more effective, and less costly. We use bureau-based scores to build advanced analytic models that identify the customers most likely to cure or pay so our clients can optimize their returns. Contact us today on 647.309.1803 to get the conversation started.

 

1 https://www.domo.com/learn/data-never-sleeps-5?aid=ogsm072517_1&sf100871281=1
2 https://www.ibm.com/blogs/insights-on-business/consumer-products/2-5-quintillion-bytes-of-data-created-every-day-how-does-cpg-retail-manage-it/ 

3 https://www2.deloitte.com/content/dam/Deloitte/za/Documents/financial-services/ZA_Digital-age_160916.pdf
4 https://www.entrepreneur.com/article/330232

5https://www2.deloitte.com/content/dam/Deloitte/za/Documents/financial-services/ZA_Digital-age_160916.pdf

6 https://www2.deloitte.com/content/dam/Deloitte/za/Documents/financial-services/ZA_Digital-age_160916.pdf
7 https://www2.deloitte.com/content/dam/Deloitte/za/Documents/financial-services/ZA_Digital-age_160916.pdf