Building competitive advantages with Machine Learning
AI-driven services are changing the sources of competitive advantages. Customer segmentation and service personalisation, risk management and fraud detection are key areas where machine learning can help develop strong competitive advantages.
To provide the same degree of personalized insights that tech companies do, banks need to get serious about adopting machine learning. A very high level of personalisation cannot be achieved with traditional methods of segmentation.
Fraud is still on the rise. Fraud patterns are constantly evolving and proliferate through multiple channels. At the same time new regulatory requirements are introduced to protect consumers and fight money laundering. Once a pur backend issue, fraud detection needs to become smarter to stop fraud at the point of origination, without upsetting the customer. This is a highly challenging tasks for banks that want to maintain up-to-date fraud detection models in production.
Machine learning for customers segmentation
Traditional banks operating models are not adapted to an era of rapidly shifting consumer behaviours and the ever growing importance of mobile platforms as a primary channel of interaction.
Technology-savvy and self-directed customers are not only visiting branches less often, but they also want greater control over their financial services and the capacity to manage their money at any time.
Reshaping business operations to move from a product-centric model to a customer-centric model is required to build greater loyalty, reduce churn, increase cross-selling and attract new digital-centric customers.
Customer-centric models however require a deep understanding of customer needs and the capacity to design products tailored to the specific needs of highly targeted segments.
Machine learning based predictive models and unsurpervised learning can help product managers identify patterns and consumer behaviors to design offers tailored to customer needs, interests and habits.
Machine learning for fraud detection
Machine learning is a powerful mean to design smarter and more reactive algorithms to fight credit card fraud. It is now possible to combine several models to detect fraud in real-time and to run models that can adapt themselves to fast evolving threats.
Anti-money laundering is also an area where machine learning technics can bring several advantages over traditional rule-based algorithms. AML systems can leverage both supervised and unsupervised technics to explore and give meanings to large transactional data sets and detect suspicious activities and behaviours.
Automated Advice Models
The robo-advisor is a strong buzzword in the Fintech arena. If the illustration of this topic is indeed a smart robot, we prefer refer to the more general term of automated advice models.
Obviously advice models can be used to dynamically optimize assets allocation and provide superior insights in the domain of wealth management.
But as stated by the Financial Conduct Authority (FCA) there is today a big financial advice gap with at least 16 million people in the UK only that would need advice but can't afford it. With machine learning, Banks get the capacity to acquire a much more accurate understanding of consumers behaviors and identify patterns that can help them launch personal automated advices designed to help customers reach their goals and take the most appropriate decisions according to their personal situation.