First published in CIVEA’s Enforcement News (Spring 2025 edition)
The UK government are committed to the development of Artificial Intelligence (AI) with a recent ‘AI Opportunities Action Plan’ stating that Britan is the third largest AI market in the world behind the US and China and measures need to be taken to secure this position and help shape the future of AI development.
AI based route optimisation and visit scheduling
To succeed, we need to be forward thinking and innovative in our application of AI and this applies across all markets. Significant developments are already underway in the enforcement sector with the launch of an AI based route optimisation and visit scheduling solution, which has already received recognition, winning several industry awards. Sphere, developed by Destin Solutions is the first solution of its kind to combine route planning to get Agents quickly from A-to-B in conjunction with achieving the optimal outcome from that visit. It has completely revolutionised how cases are now allocated to Enforcement Agents as well as injecting intelligent learning and prioritisation into case handling and routing.
Prioritising enforcement visits based on case attributes
It works by combining data (debtor information, client requirements and business rules) with machine learning and mapping, to route and track recovery activity and meticulously plan an Agent’s journey. It can prioritise an Agent’s visits based on attributes of the case the visit is related to and the visit itself, for example, what type of visit it is and how long it has been awaiting a visit. Senior Management can also apply restrictions on when visits can be scheduled, for example, within defined timeslots.
Enforcement Agent autonomy and real time learning
Sphere also gives Agents the autonomy to ‘postpone’ scheduled visits, assign visits a higher or lower priority based on their detailed case knowledge, override the suggested next visit or include a number of appointments pre-agreed with a customer to visit at a specific time.
Sphere uses real-time learning with every visit, it identifies the times of the day and week when a debtor has been absent in the past, to calculate the likelihood of them being home on the next attempted visit and updates route itineraries accordingly. Intelligent algorithms have been built in to consider factors such as traffic congestion, visit priority, an agents current location and historical visit data.
AI driving down the cost of enforcement visits
Ultimately the implementation of this type of AI driven solution has been proven to drive down the costs of enforcement visits, through strategic routing, resulting in reduced fuel costs. It has also led to a significant reduction in the amount of man-hours that goes into manually planning routes as the system does everything for you, leading to increased agent availability and an increase in the amount of visits an agent can achieve in a given time period.
Automatic case allocation for Enforcement Agents
Sphere also has the ability to automate the process of allocating cases to Enforcement Agents. Historically this has been an extremely time-consuming task and can often include allocations teams manually reviewing all their current caseloads. Sphere automates this entire process based on the parameters that Senior Management set and its ability to process vast amounts of data from multiple data sources very quickly. It can also automatically identify the most advantageous debt to pursue, based on any number of factors such as age, type and value of debt, likelihood of collection and the knowledge and experience of the individual Agent.
In conclusion using this type of AI based technology has the power to deliver significant advantages to Enforcement Agencies. For example, the ability to take on larger client case loads with minimal impact on work load and the ability to share detailed performance data with clients on how agents are working their cases. It is more environmentally friendly, helping reduce fuel costs and carbon emissions for Agencies and their clients, optimising journey times and reducing the likelihood of unlocatable properties due to detailed routing and mapping. This type of technology could also prove to be a key factor in supporting bid processes with its ability to demonstrate improved contractual performance, improved collection rates and reduced impact on the environment.
