When it comes to innovation, the debt collection industry seems to fall short compared to other segments in the financial sector. There’s a lot of room for improvement in terms of customer satisfaction, and implementing new technologies can quickly address its hard-to-erase characteristics.
According to 2019 research by Allied Market, debt collection software generated close to $1B in Europe only. This number is projected to double by 2026. However, many creditors resort to traditional communication methods and manually processing data, which is time-consuming and error-prone.
Organizations collecting debt are pressured to cut costs while improving processes and increasing revenue. The solution is clear: fast—track day-to-day activities by reducing labor-intensive administrative parts and transferring that burden to technology.
Adopting AI is your best bet to free yourself from being chained to the phone for hours or drowning in a stack of documents. AI in debt collection can help you improve debt recovery rates and establish self-sufficient processes. On top of that, there are many debt collection techniques that we’ve learned from working in the industry that could boost your firm’s effectiveness.
In this text, we will explore AI’s pivotal role in boosting debt collection efficiency in several areas. We’ll look at everything from its benefits in payment automation and communication, focusing mainly on document processing.
Ways AI Helps Debt Collection Agencies
In an industry that demands precision and speed, automated debt collection technology provides you with the means to gain a competitive advantage. It reduces operational costs with automated document reading and accuracy enhancement to process automation.
AI in debt collection improves the speed at which the necessary data is collected. With the help of chatbots, debtors can provide you with their demographic data and make payment arrangements. This saves time and money and creates a low-stress environment in an uncomfortable situation.
Advanced AI is designed to help debt collection companies read, extract, and classify data from a document they’ve never seen before. Adding continual learning to the equation allows you to teach AI how to better work with your unique documents. You adjust the process to your unique needs by training the model yourself.
Such possibilities are life-saving when you’re processing thousands of monthly invoices, checks, employment, and rent contracts. Adopting AI for document processing will allow you to have the data you need when needed — no fuss, no muss.
Skip Tracing Automation
The bread and butter of debt collection processes is, of course, skip tracing — the process of locating debtors who have gone off the grid. There are four major pain points in manual skip tracing:
- Difficulties with finding relevant information in piles of documents
- Sunk costs and limiting company growth
- Monotonous work that increases the risk of burnout
- A larger number of cases creates a push for a higher headcount
Finding this key piece of information requires massive effort. It also generates errors and unnecessarily incurred costs along the way. In an administrative-based activity where patience and meticulousness pay off — debt collection can benefit from AI.
If you – or your team – perform this needle-in-the-haystack job without any automation, the pile of documents that require a deep scan can be overwhelming.
Intelligent Document Processing can help you automate part of the debt collection process – here’s how.
Full-Text Search (Skip Tracing Case)
One of the most powerful applications of Intelligent Document Processing in skip tracing is the Full-Text Search feature. Dozens, if not hundreds, of documents make up each debtor’s case, and as a skip tracer, you must go through them all to establish means of locating the individual. This is a long, complex process.
Instead of straining your eyes for hours and risking burnout, you can let AI do it for you. In our tool that specializes in such cases, you can read scanned documents and find data. All you have to do is use a shortcut to look up the keyword in the file, and you’re good to go.
Think of the time you freed that can now be dedicated to getting in touch with debtors and increasing your success rate. Eventually, you get a higher chance of successfully closing each case in a shorter time.
This function helps skip tracers cross-reference data from public databases with the sets of documents received for analysis. A good example is a Polish debt company, KRUK S.A.. They automated the monthly processing of over 400.000 documents with the help of Alphamoon and implemented a skip-tracing solution, resulting in a 70% faster data extraction and information collection process.
Intelligent Document Processing in Automated Debt Collection
Intelligent Document Processing (IDP) is one of the growing technologies that help you save time and money. It’s a technology-driven approach combining artificial intelligence (AI), machine learning (ML), and natural language processing (NLP) to automate the extraction, understanding, and processing of information from various documents.
AI-powered IDP tools boost processes in all industries, including debt collection, by extracting data from various documents — court orders, credit reports, and financial statements. It also builds comprehensive profiles of debtors and saves time by doing so.
IDP helps companies in debt collection cut through the data noise and enable access to relevant data much faster. As a debt collector, you look for clues, addresses, and phone numbers in stacks of paper and digitized documents.
The list of documents varies for each case, but they share the same burden – it’s a complex, investigative line of work that takes time. Since IDP aims to turn unstructured data from a document into coherent data pieces ready for further processing, you can save yourself the trouble of spending hours on a task that can be done in minutes.
Take a look at the three core capabilities that empower teams to work smarter and with a focus on less monotonous tasks.
OCR & Data Extraction
AI-based OCR, which stands for Optical Character Recognition, deploys artificial intelligence to “translate” all kinds of visual objects into data. This modern technology that overcomes the old rule-based models can read and understand objects such as tables, handwritten signatures, or notes the same way we do.
AI OCR pairs up with data extraction – a step where AI recognizes particular fields from the document and gathers them all in a structured data set. This combination allows you to submit your documents and have the data extracted in your preferred format.
The data set is referred to as Knowledge Base. In the above example of Alphamoon’s bailiff enforcement notice automation, we have added the bailiff’s name and edited the installment amount. The rest was read and extracted by itself.
There are many ways to use extracted data points. Alphamoon’s tool can be fully integrated with any internal system using API. This kind of integration will allow using the extracted results without any additional work. There is also a way to download results in CSV form and forever say goodbye to manual data entry!
Document Classification & Document Parsing
The above results were achieved due to a preprocessing stage of document classification that supports the AI-based OCR function. Automatic document classification involves using machine learning, computer vision, and natural language processing to categorize documents based on their content, layout, and appearance.
As a debt collector, you deal with many forms, contracts, notices, statements, and letters. Each has a varying layout and content – whether it’s a bailiff enforcement notice, invoice, purchase border, etc. Keeping track of them all takes a keen eye and attention to detail.
This is just simplified by document classification and document parsing. The two functions work together to sort your large PDF files with multiple documents in a readable format.
Speaking of large PDF documents that are a pain to unpack. Scrolling endlessly, using add-ons to split them, or extracting data from the whole PDF, all scattered and unstructured, can take hours to fix.
With Alphamoon, the file is automatically split into multiple documents after being processed. The tool detects documents in a PDF and organizes them in the UI. The debtor’s info is extracted separately for each doc, saving you time and effort.
Continual Learning in Debt Collection
A crucial aspect of AI-powered IDP tools is their ability to learn from user interactions. As you process documents and correct or validate some along the way, the tool learns to improve accuracy over time.
Why is this so crucial?
The main purpose of continual learning in the context of IDP is to improve the accuracy and classification of the data extraction. It’s likely companies format their documents differently from one another, even though the type of document is the same. These inconsistencies in document structures can become too inconvenient to handle.
With the help of continual learning, you can optimize the tool you’re using to your needs. It learns to recognize the context surrounding the data point you want to extract. After use, it reaches the pinnacle of automation by providing 100% accurate results on every try.
Apart from the inconsistent documents, this plays a huge role when changes in business compliance occur, and you are asked to adapt to the changes immediately. You may need to implement an entirely new set of documents, which may be tough to adjust. But the tool you’ll train needs only a few tries to give you what you ask.
Natural Language Processing (NLP)
Natural Language Processing allows IDP to process complex language structures and extract specific information accurately. It’s the ability to read and interpret human language within documents and understand contextual relationships, and can be extremely useful for your debt collection strategy.
NLP algorithms also analyze written communication between you and the debtors, such as emails and chat logs. AI can identify patterns, sentiments, and language cues to help you assess whether they will repay the total amount within the required timeline and, if not, how they will shift from the preferred course of action.
In the context of debt collection, digital voice agents based on NLP algorithms assist by gathering data about the debtor’s payment history, contact frequency, and preferred modes of communication. Conversational AI gives customers crucial information on who is collecting and how much they owe before accepting payment.
Speech Analytics and Voice Recognition
AI-driven speech analytics and voice recognition systems can transcribe and analyze your phone calls with debtors. They can identify keywords, tone, and emotions in real-time, providing insights into the effectiveness of the call and the debtor’s willingness to cooperate. They can also provide the most appropriate script for the current call.
Voice is the quickest and most natural form of human communication, which is why it is preferred by debtors seeking support. This is where AI-based voice assistants come in – they reduce call waiting times while simultaneously relieving you of repetitive tasks such as verifying debtors’ information, presenting debt repayment options, and more.
Communication Automation – Calls & Messages
After you collect and process the data, it’s time to validate the points of contact. In the past, as a skip tracer, you’d have to conduct phone screening, send mail, knock on doors, and meet with relatives or close collaborators with the tracked individual.
AI debt collection allows you to automate this process, too. Thanks to virtual assistants, you can automate calls and emails sent to the debtor’s addresses and set automatic replies.
Those who find phone conversations disruptive and uncomfortable take solace in having new forms of customer service available. AI-powered bots can gather information about their payment history, preferred modes of communication, and, importantly, their intent to pay.
Payment Automation: Speeding up the Transactions
Automated payments streamline your debt recovery workflows through payment notifications. The automation can be adjusted to the needs of your business and provides you and the borrower with numerous convenient payment options to ensure swift debt recovery.
Automated payments instantly capture failed payments due to expired credit cards or address changes. This reduces involuntary churn and ensures uninterrupted customer services, significantly improving customer experience while collecting more cash for the business.
Debt Surveillance and Automation of Debt Purchased
Debt surveillance supports a coherent overview of all the open cases and allows you to monitor each case’s stage. Since it’s the final stage of the collection process for insolvent debtors, you may prioritize accounts that need immediate attention. Such urgency can result in errors; going through each case is time-consuming.
With the help of debt collection AI, your software can assign priorities based on asset classification, due amount, product type, fraud identification, etc., allowing you to give customers better control over their collection progress.
Final Words
As artificial intelligence and machine learning increasingly modernize debt collection, lenders and borrowers see impressive benefits in customer experience and debt repayment. This results from efficient and streamlined processes that carve out space for more empathetic and creative approaches to the borrower interaction.
Using IDP in your debt collection practices allows you to avoid burnout and focus on the borrower. AI in debt collection provides the tools to improve your results, get insights into better communication practices, and increase debt recovery rates.
With the rise in demand to reduce bad debt and improve cash flow, there’s a need for a sturdy automated debt collection technology such as Alphamoon. The heavy-duty Alphamoon engine helps you ensure accuracy, compliance, and efficiency through data anonymization.
Get in touch with us and find the best way to implement automation in your debt collection business.
