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AI in Finance and Collections Ethics and Trends

AI in Finance and Collections: Ethics and Trends

Paul Sweeney | Chief Strategy Officer
AI in debt collection Webio Optima Qualco


Paul Sweeney, Webio CSO, and Dan Blakojevic of Optima Partners spoke with Terry Franklin, EVP at QUALCO, in the Credit Shift podcast: Navigating the Intersection of AI, Ethics and Finance.
Below is an overview of what they discussed.

Part 2

Read Part 1: AI in Finance and Collections: Opportunities and Sticking Points

Ethics in AI 


AI can Help Finance Companies Manage their Duty of Care 
 

AI in customer engagement is about more than just query resolution, it is also about being an ally in managing Duty of Care and ethics. How can AI help in this area? Companies can use AI to recognise patterns and language cues in customer conversations and then predict needs and help agents guide discussions sensitively.  

This proactive approach helps agents avoid inappropriate or harmful questioning. For example, if it emerges that a customer has a health issue, the AI can make the agent aware of this and it allows the agent to move that conversation in a different direction rather than the customer having to go through maybe four or five lines of intrusive questioning. 

In industries like credit and risk, AI acts as an assistive, not a substitutive, technology. It enhances human intervention, enabling targeted, meaningful, and quicker interactions.  

Duty of Care extends beyond merely explaining costs to customers; it's about ensuring ethical conversations. Regulatory bodies like the FCA in the UK are intervening and holding organisations accountable for not clarifying product details properly. Additionally, AI plays a role in predicting conduct risks and helps to prevent fines by flagging potential issues in real-time.  

The conversation around AI should align with well-established international AI and EU legislation, and aligning with ethical standards from the start is key to managing AI's impact on an organisation and its customers.  

 

AI Ethics: Tips and What to Watch out For


What Data are you Using to Build Models? 

When developing ethical AI technology, it's important to address model bias and prioritise high-quality datasets that focus on ensuring the data is truly representative across the board.  

For example, in the hospital system in the UK they were trying to figure out why certain people were late for appointments. They looked at the age groups and demographics and some psychographics, and they just couldn't figure out why certain people were always late or not turning up. What they eventually found out was that these people were all taking public transport and they had transport issues like late busses. So, in this case, there was a staggered impact that was initially missed as they weren’t collecting the right data.   

This example highlights the importance of collecting specific data to get the right picture. In data collection, be intentional about what data you request, how you store it, and for how long. Consider the source of your data and obtain proper permissions, distinguishing between real-world and synthetic data.  

Mega Trends Hitting the Finance and Collections Industry 


Reduce Costs and Efficiency Optimisation
  

Generally, people are feeling the pressure to reduce costs as much as possible. So anywhere technology, AI or analytics can be applied to help choose the right digital processes and minimise the number of physical resources, is gladly embraced.   

There is also a notable increase in costs for SMEs, driven by factors like the cost of living and energy prices, making it important for all organisations to seek greater efficiency. Optimisation, especially in managing multiple communication channels, is a key focus in trying to prioritise customer interactions efficiently while facing an increase in customer contacts due to these financial pressures. 

Efficiency optimisation is the talk of the town across sectors like energy, retail, insurance, and banking. Businesses are under significant cost pressures from fees paid to tech giants like Google and Amazon, as well as staff-related expenses, so adopting new technology has to see better business outcomes as the end goal. 

AI and Job Losses  

There has been some hype in the media that AI is going to make a lot of people redundant, but we have not yet seen this happening. Instead, what we are seeing is that where AI is automating tasks, it frees up space for companies to do work that they couldn't get at before. However, we are seeing that in some companies when people leave the business, they try to make up for that vacancy with better efficiency and improved operations rather than by employing a new staff member. So, they haven't let anybody go, but they haven't replaced people who have left, which points to possible hidden figures. 

Differentiation: What Changes when Everything's Digital?  

Technology provides opportunities to make experiences more flexible and accessible for people. Digital experiences could give people more flexibility instead of rigidly following "cow paths" of past systems - we don’t want to just “pave over the cow paths”. 

For example, someone who cannot pay $20 at the end of the month currently has no choice but to pay penalties if they pay in two instalments a few days apart. Yet with digital payments, we could allow equal outcomes through instalment options without penalties. Something as simple as splitting a $20 payment into two $10 payments a few days apart should not disadvantage the customer if unforeseen circumstances prevented the full payment on the due date.   

Technology provides opportunities to rethink experiences and make them more accommodating for people's real lives. 

Disconnect between Old Systems and New AI  

Businesses face challenges integrating old and new technologies within their organisations. Different departments often operate with vastly different levels of sophistication. For example, finance departments may still rely on Excel spreadsheets from the 1990s while other areas adopt cutting-edge AI solutions.   

This creates incompatibilities that stem from the massive disconnect between the technologies and processes used across an organisation. In some cases, an infrastructure and operating model built for one type of process does not mesh well with another designed for a different era.   

However, things are changing as consumer demand drives the need for modernisation. Individuals want flexibility that legacy systems cannot provide. So, we see businesses are adapting to where customers are heading digitally.   

In reality, integration challenges remain, such as setting up APIs or file transfers between disparate systems, or priorities within an IT department may not align with all business units equally. An initiative to automate processes and improve customer experience through new technologies can get pushed down the priority list if resources are limited.  

While outdated practices still exist internally, external pressures are motivating companies to upgrade their technology and break down barriers between old and new. As consumers continue flocking to digital options, businesses will have to continue evolving their own systems to keep pace. 

Final Thought: AI and Technology must Deliver a Material Difference and Add Value  

For it to be welcomed, AI needs to impact real-life situations. The goal is to solve practical problems through extensive research, transforming raw data into actionable activities. Companies must emphasise the entire pipeline's importance, from identifying trends to final implementation, to ensure successful AI application. The focus should be on achieving meaningful outcomes rather than technical brilliance alone. 

Read Part 1: AI in Finance and Collections: Opportunities and Sticking Points

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