Undoubtedly, machine learning, AI analysis, and algorithms are the future of best-in-class customer experiences. These technologically advanced applications present opportunities for sweeping efficiencies, personalisation, intelligent responses and rapid, accurate assessments to manage enquiries.
While we’ve all heard of AI and may grasp the basics of machine learning, few comprehend the scalable nature of overhauling customer interactions. In this blog post we’ve broken it down to explain the advantages specifically for the credit sector. These insights come with thanks specifically to Wonga Online and their head of credit department as one of the leading credit providers pioneering machine learning and enhanced consumer experience.
The AI Digital Breakthrough
The key to AI developments is that cognitive functions can make lightning-fast risk assessments, reaching decisions at speeds that defy manual evaluations.
Even where we’re unaware, we encounter machine learning at multiple touchpoints in any credit application process, from solvency checks, fraud protection, customer service functions and day-to-day account management.
Credit Sector Machine Learning Advantages
In terms of benefits, they’re countless and hard to ignore:
- Substantial reductions in customer liaison costs.
- Expedited decision making to improve visitor experiences.
- Avoiding manual errors or poor judgement.
- Automatically spotting trends or patterns.
- Identifying unusual behaviour.
Customer security and fraud prevention is a core machine-learning task.
In essence, AI learns how we tend to use our debit or credit cards, builds up a continually advancing picture of our habitual behaviours, and can pinpoint anything it feels is out of character.
Automating this process makes it extremely fast to contact a customer, verify the authenticity of a transaction, or stop fraud before it happens.
The improvements are multiple, from protecting customers, providing instantaneous alerts, figuring out something is suspicious in nanoseconds, and recording the outcome to inform further how AI responds to similar scenarios in the future.
Next, we’ll review some machine learning processes in use across the world’s largest financial and banking institutions to look at how they’re incorporating AI in primary-level services.
Machine Learning in Routine Banking
Narrative Science reports that around 32% of credit and financial service executives leverage technologies such as predictive analytics, voice recognition, and recommendation engines to deliver high-accuracy forecasts and security protocols.
One of the challenges is integrating these evolving assets with legacy systems.
You’ll find Kensho machine learning and data analytics in place at the Bank of America and Morgan Stanley among many other renowned banking corporations.
More and more, the true innovation driver is customers themselves – demanding more from their credit providers and expecting immediate service at their convenience.
Credit lenders looking to stay ahead of the competition invest substantial budgets in machine learning to keep pace with expectations.
Theunis Rensburg, of Wonga, explains that:
“Traditionally, predictive modelling techniques have mostly been used in credit businesses for scoring purposes.
Today’s reality is different; if you’ve got the data, you can employ predictive models to optimise any part of your business.”
Combatting Credit Application Fraud
Deception and fraud are fundamental challenges in the credit landscape, extending across gift cards, false credentials and ID theft.
Several major banks incorporate software from F5 (formerly Shape Security), which can differentiate between humans and bots, using machine-learning models trained extensively to spot even highly sophisticated scammers.
The Blackfish network incorporates bots that can identify when login credentials have been compromised, notify clients in real-time, and block transactions, all within the same moment.
In the first week that Shape Security was launched, it prevented an attempted hack that targeted around one million consumer accounts.
Boosting Customer Acquisition and Retention
Our final element to consider is customer acquisition – a primary focus for credit lenders in a crowded market.
Machine learning is a powerful ally in getting to know financial behaviours, thereby targeting promotions and proposals directly to address customer needs.
The impact can be significant whether responding to indications that a potential customer would benefit from a personalised offer or making proactive efforts to deliver educational resources or financial advisory content.
Pre-emptive and added-value marketing are valuable ways to generate a customer journey that starts at the exact point that will be most convenient, securing an outstanding brand reputation and setting a solid foundation for a long-term client relationship.
Behind all of these applications sits an overriding principle.
The ability for machine learning to expedite access to user information, make instant decisions, and help customers have the best possible credit experience.
While there will always be a place for personal interaction, the compelling benefits of machine learning make a faster, more effective, and highly customised credit industry possible – the uses we’ve seen to date are certainly only the beginning.