AI Debt Collection – Its Transformative Impact
In navigating the complex and sensitive task of recovering debt, the adoption of Artificial Intelligence across debt collection operations serves as a way for empathy, automation and efficiency to go hand in hand.
This article will explore how AI debt collection software emerges as a powerful tool for agencies to fine-tune their debt recovery strategies, automate manual tasks, reduce operational costs, increase collection rates and foster positive customer relationships.
The Debt Collection Industry
Debt collection is typically initiated 30 days after a borrower defaults on a fiscal obligation such as a loan or credit card repayments. Many financial institutions outsource this process to a third-party debt collection agency who specialises in recovering outstanding payments against a required timeline.
Conventional debt collection strategies are labour-intensive and require a considerable amount of manual effort. Typical recovery processes included writing notices via the post, manually calling debtors multiple times every day and door-to-door knocking. These debt recovery strategies lead to disgruntled customers, low customer satisfaction and high operational costs.
In today’s fast-moving world, customers expect flexibility, financial guidance and compassionate communication. Settling a debt needs to feel effortless and promote the individual’s financial wellbeing. High performing debt collection organisations are able to transform a conversation about debt, into one of financial empowerment. To aid this process, debt collection AI solutions can be used to assist both the creditor and customer. Debt collection companies have the ability to incorporate machine learning, conversational AI and advanced analytics across their collection operations. Taking advantage of these technologies will support collectors to increase debt recovery rates, reduce costs and simultaneously improve customer service, helping individuals build a strong financial foundation for debt avoidance.
Challenges in the Industry
The debt collection industry traditionally has centred operations around manual processes which can be limiting through inefficiencies, exorbitant costs and poor operational processes.
Here are the key challenges debt collection companies experience:
Tighter margins
Recent economic conditions pose significant challenges for the Debt Collection industry. In an economy shaped by a cost-of-living crisis and minimal wage growth, many consumers face difficulties in making repayments. This also impacts debtors’ willingness and financial ability to repay their debts, hindering collectors ability to recover debts. Spending more time to chase debt results in tighter margins.
Human error
Traditional debt collection operations involved labour-consuming processes. Often, staff were required to manually dial debtors’ phone numbers using desk phones, handwrite notices that were sent through the mail and manage a large volume of paper records stored in filing cabinets. These methods utilised a significant number of resources and required substantial manual effort, increasing the chance of human error.
Compliance and data protection
The debt collection industry is heavily regulated, with laws that vary by region and that are subject to change. In Australia, debt collection agencies must adhere to stringent regulations including the ‘Debt Collection Guideline: For Creditors and Collectors’ which outlines how businesses can operate in accordance with the Commonwealth consumer protection laws. These rules include the time of day a person is allowed to be contacted, limitations on call attempts and also considerations around public holidays. Non-compliance can result in hefty fines, lengthy legal proceedings and company reputational damage.
Similarly, debt collection agencies handle and store a wealth of sensitive personal information every day. Agencies burdened by outdated technology can be more susceptible to security breaches, compromising debtor privacy and data.
Communication challenges
In a globalised world where it is common for people to speak various languages, collectors often face language barriers during phone conversations, complicating the debt recovery process.
Companies employing a conventional debt collection ‘one-size fits all’ approach i.e. utilising a standardised call script, can experience poor debtor engagement and high bad debt.
Legacy systems and outdated technology
Obsolete processes and outdated technology make it near impossible for agencies to embrace automated debt collection. The collections industry often grapples with implementing new technologies alongside legacy systems without disrupting existing workflows. Bridging the gap between established processes and technological advancements requires adequate staff training and internal willingness to innovate.
Ways AI is driving the future of debt collection practices
The advent of Artificial Intelligence in the debt collection industry is resolving many challenges faced by debt collection agencies. AI-powered solutions are overhauling the traditional practices in debt collection, playing a pivotal role in transforming the way recovery agencies operate.
Incorporating AI into debt collection processes is possible through connecting Large Language Models (LLMs) and Machine Learning with an organisation’s data repository system. In doing so, this opens a breadth of opportunities for debt collection agencies to streamline processes and generate unique insights across customer experiences.
AI is driving the future of debt collection through:
Automation of routine tasks
AI in debt collection is redefining the way debt collection agencies operate through automating numerous routine tasks. Let’s take a look at some of the manual tasks that are overhauled through AI:
Automating identification of debts
AI’s ability to analyse transaction data in real-time means overdue payments are flagged and collectors are automatically notified about collectible accounts. This AI-driven workflow requires minimal labour, reducing operational costs and increasing efficiency. These models can also be used to identify the best channel and the ideal time to contact customers to improve contact rates.
Analysing account risks
Analysing the characteristics within a borrower’s profile – credit history, delinquency record and payment behaviour – AI can calculate the risk the debtor will miss their payment. By leveraging AI to predict account default risk, debt collection agencies can apply a proactive risk management strategy and create a strategic imperative to ensure the customer does not default on their upcoming payment.
Prioritising accounts
Taking into account the level of account risk, AI can categorise debts from high to low priority as well as assign accounts to specific collectors.
Based on factors such as value of the debt, the account risk and how long the debt has been outstanding, AI can identify which accounts require immediate attention and designate a prioritisation score for less pressing accounts. By prioritising accounts across collection activities assists management to optimise resource allocation and enhance recovery rates.
Cost reduction and operational efficiency
Debt collection AI software automates repetitive tasks that require manual labour such as data entry and document processing. Implementing this type of automation streamlines collections operations and empowers creditors to handle larger amounts of accounts without proportionately expanding the workforce. As a result, business managers can reduce costs and increase operational efficiency.
Compliance and risk management
Meeting compliance standards is paramount to any financial services institution. AI-driven speech analytics and voice recognition systems surface risks of non-compliance through transcribing and analysing phone calls between agents and debtors. These tools automate compliance management through monitoring customer calls against pre-set criteria that includes standards such as disclosing repayment policies, obtaining debtor agreement and other conditions. At the end of a call, AI-generated models are produced which reveal the level of agent adherence to compliance standards. The findings these AI-driven systems produce are critical for surfacing where a risk management approach needs to be taken to avoid non-compliance.
Prior to this technological advancement, debt collection managers were required to listen to every call to ensure the agent met the business’s compliance objectives. This process was extremely time consuming, labour-intensive and vulnerable to human error. Similarly, with AI compliance, checks can be more comprehensive as all calls are able to be reviewed, not just a sample.
Personalised customer interactions
By leveraging Natural Language Processing (NLP), debt collectors can extract specific information from written communication through analysing documents or email and chat conversations. NLP’s ability to identify language cues, debtor sentiment and intent allows management to determine the preferred course of action for subsequent outreach which can contribute to a better customer experience. For example, NLP engines can recognise when a customer is experiencing financial hardship. This crucial information enables collection agencies to enact their hardship strategy and tailor the customer’s payment plan according to the debtor’s specific financial situation.
Over time, the more customer data that is accumulated about every debtor, enables collectors to highly customise communications that resonate more effectively with the individual’s profile. Moreover, personalising interactions can help boost customer satisfaction and increase the chance of recovering debt.
Digitisation of legacy systems
Artificial Intelligence is modernising legacy tech, allowing agencies to improve debt collection efficiency and eliminate maintenance costs associated with outdated systems such as a desk phone.
AI-powered communication software automates contact between collectors and debtors through triggering personalised SMS and emails that include a payment link or QR code, facilitating debtors to make repayments with flexibility. By implementing this type of AI solution, collection agencies can digitise engagement and speed up transaction processing, helping reduce days sale outstanding.
Predictive analytics for enhanced recovery
Predictive analytics is rapidly transforming the collection process. Predictive analytics in debt collection analyses a vast range of customer data including payment behaviour to identify trends that can signal a risk the account will become delinquent or conversely have a high likelihood to make the repayment before the payment date. These insights enable managers to tailor future communications according to the debtor’s profile which can enhance customer relationships and increase recovery rates.
Final Words
AI in debt collection is paving the way for a future of empathetic, automated and efficient recovery communications, enabling highly personalised interactions that focus on the financial wellbeing of the individual yet balance the objective of increasing cash flow.
DebtSCAPE is an AI-powered contact centre solution that is used by debt collection agencies to maximise collection rates and optimise recovery communications. The solution includes an extensive feature-set including a predictive outbound dialler, customisable call scripts, quality assurance module, PCI DSS payment tool, call recording and storage, analytics and reporting. DebtSCAPE empowers collectors to deliver empathy and improve efficiency across customer interactions that occur over the phone, email, web chat or SMS.
If you would like to understand more information about how you can incorporate AI across your debt collection operations, contact IPscape today to view a demo of DebtSCAPE.
Organisations use IPscape’s communication technology platform, SCAPE, to unlock growth by building personalised communication with customers at scale, through their channel of choice.
