Machine learning is the application of artificial intelligence to provide systems the ability to learn automatically and improve their understanding without being programmed explicitly. In the context of keeping in mind the customer centric approach and the collections experience, machine learning is a very functional tool to help personalize the experience of every consumer, effectively, thereby helping in resolving their debt.
How does a debt collections company apply Machine Learning to their business?
Machine learning can be leveraged to understanding behavioural science throughout the entire journey, which starts from initial engagement of the consumer to the last stage. The way to transform debt collections is by focusing on the machine learning part as well as the digital first experience’s part for the consumer. For example, when a company sends millions of emails per day and thousands of text messages per week, these data points help the Machine learning understand every click and every action taken via the messaging provided by the company.
This engagement data helps understand how the customer would react at every step and then personalize the messaging accordingly. Machine learning is hereby used to personalize and optimize each step of the customer journey, thereby ensuring that they help customers become brand advocates. This process will also help the brand realize who are the probable customers who would mostly default their payments. In such a scenario, it is easier to provide them options and decrease the bad debts.
Now let us look at advantages of using AI and ML in debt collection:
- To improve communication: Debtors and debt collection is a very sensitive topic, and it is not very easy to have this conversation with the people who are not able to make payments. AI and ML powered tools, like chat bots can help the lending business by being personalized. Personalized basically means tailor made based on the user style, age, sensitivity and a lot more factors.
- To better understand your customer: An average person has more than one credit card, more than one loan and maybe more than one bank account. Now in order to understand the borrower’s profile and their debt history, AI needs to be used as this cant be achieved manually.
- To optimize the debt collection strategy: AI and ML powered tools can help improve human decision-making in debt collection. The software works with historical data about solvent and insolvent borrowers, making it easier to spot at-risk accounts. It helps in flagging potential late or non-payers, enabling the company to implement a proactive, not reactive, strategy.
- To streamline processes: When we talk about streamlining processes, it can be as simple as getting everything online rather than having to physically visit the financial institution. By implementing AI, one can align everything according to customer experiences and eliminate all the unnecessary steps. AI can help with periodic reminders too in such a case.
AI is a tool, not a solution. However, there are benefits involved in the process:
- Better pay back rates: It is a given that every financial institution bears losses every year, thereby affecting their balance sheets. These losses can be huge which in turn can impact the GDP of countries on a macro level. However, using AI and predictive model analysis, risk assessment and fraud detection, one can reserve their fortunes, for up to profits by 38 percent.
- Fewer insolvencies: Earlier financial institutions used to implement motivational measures to appreciate the borrowers to repay loans. AI should be used for such a scenario where risk laden profiles are spotted before they make the payment delays. Such systems can even let companies assess whether an individual should get a loan in the first place, helping the company avoid lending to risky clients instead of negotiating repayment schedules later.
- Automated communication: One of the best things now in this industry is chatbots. Chatbots shall approach the consumer at the right place and at the right time. For example, a chatbot can ping a client when they are online on social media. This is one of the most effective ways in reaching out to them to communicate our messaging. This even helps in understanding and categorizing the borrowers.
Beyond this, machine learning and artificial intelligence can also enhance engagement in newer ways. For example, AI can analyze audio from customer calls to determine how different scripts or offers have impacted customer response and collections status. It basically works like a behavioural analysis and this information can guide future training and ongoing optimization to prevent or resolve the delinquency. To end this, we can say AI and ML are important tools of this industry and debt collection companies can recreate customer experiences, align business operations and drive perfect and faster resolutions. It is thus the need of the hour!