Machine learning is a fast-growing differentiator between conventional credit lenders and contemporary organisations offering advanced customer experiences across the board.
In brief, machine learning means that each interaction and transaction generates a series of data streams that help companies learn what we like, how we work, and how to add value to our experience.
This focus is the core driver behind mass rollouts of machine learning in the credit sector, as lenders and banks look to improve reliability, brand positioning and the overall quality of each customer touchpoint.
Machine Learning Applications in Credit Lending
For a live example, if one visits the leading online credit provider Wonga’s website, you’ll feel a lot more like you’re dealing with a much more open book than even a few short years ago.
The traditional disconnect between lender and applicant led to distrust and, in some cases, a reluctance to use financial borrowing products due to concerns about the small print, hidden costs or complex assessment processes.
AI and machine learning enable businesses to humanise interfaces and enhance consumer loyalty, with options including:
- Suggestions to signpost visitors to the information or services they are looking for.
- Specific dialogues and customised communications, such as correspondence that addresses the customer by name and can answer their account questions digitally.
- Intelligent credit assessment underwriting tools to make faster decisions, reducing the risk of fraud and allowing users to access wider credit options.
- Offers of pre-qualified loans and instantaneous pricing, based on credit assessments and previous borrowing history.
While we can see the benefit of these user-led offers, the tech behind them is complex.
How Does Machine Learning Work?
Artificial intelligence works by recognising patterns and using data-based algorithms to solve the puzzle at hand.
It is particularly beneficial in credit lending since businesses collate huge volumes of data, which they can then analyse to give insights into consumer actions and expectations.
Innovation doesn’t stand still, and although machine learning might be relatively new, it is already in use across the industries within applications you’re likely familiar with, such as:
- Human interactions mimicked on Apple’s voice recognition system, Siri.
- Auto-tag suggestions on Facebook photos.
- Machine learning algorithms that prevent PayPal fraud.
- Traffic speed calculations captured from smartphones by Google Maps.
- Purchase suggestions made by the Amazon marketplace.
- Smart home appliances, such as Amazon’s Alexa voice AI interface.
Each of these brands gathers data, studies and interprets the resulting information and uses it to drive increased revenues or enhance the customer experience to encourage consumers to use the app, spend more money, or feel satisfied with the service received.
The Advantages of Machine Learning in the Credit Lending Sector
In a banking or credit-lending environment, machine learning helps providers showcase appropriate products to their customers or attract potential consumers with personalised offers.
AI applications automate and optimise a considerable number of processes. For example, you could initiate a balance enquiry or schedule your next payment without needing to wait in a call queue or fill in a form.
Our financial interactions are increasingly digital, so this move is essential for lenders to avoid being left behind, with a huge range of benefits linked to a fully paperless business model:
- Adherence to environmental standards and reductions in paper waste.
- More reliable data capture and storage through digital records.
- Efficient customer services and personalised responses.
- Faster decision-making with automated reactions according to each customer data profile.
The key is that machine learning is extremely accurate, so even a routine task like running a credit assessment is quicker and eliminates any potential for manual error that could impact the customer experience.
Enhancing Credit Lender Service Levels
Sophisticated algorithms track and analyse online behaviours, allowing financial institutions to better understand their customer actions and react via live communications to any requirements – regardless of operational hours.
Lenders can leverage machine learning to improve customer support levels and seize opportunities to offer customised products that match demand.
For example, suppose a customer is searching for information on car finance.
In that case, the lender could develop a loan proposal and reach out with the product offer, showing the customer a proactive approach to meeting their needs at the optimal time.
Fraud prevention and cybersecurity remain core components of any machine learning application, and existing e-commerce payment models, supported by AI, have proven highly effective at blocking security threats.
AI is a powerful tool to prevent fraudulent social engineering, such as scams whereby criminals pose as legitimate lenders or attempt to extract private customer information.
Financial deception becomes extremely difficult when always-on technology monitors inconsistencies and unusual behaviour patterns.
Harnessing the Potential for New Customer Experience Levels in Credit Lending
AI and machine learning have rolled out tremendous improvements in speed, accuracy, personalisation and decision-making across the financial sectors, from banking and investing to borrowing and purchasing to privacy.
Credit lenders that invest in innovation are leaps and bounds ahead of the competition, with the promise of immediate customer support when required, efficient loan decisions, and instant account access in real-time.
While AI is a fairly new technology, it will undoubtedly develop further as we better understand the applications and possibilities. It is a dynamic option to put customer experience as a primary performance target to gain reputation and brand awareness.
A customer that feels heard, valued, protected and listened to will have little reason to turn elsewhere – and so smart data capture and analyses are perhaps the biggest prospects for lenders to level the playing field and compete with the largest financial institutions.