Incumbents need machine learning to ward off fintech challengers

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New players are rapidly gaining higher market share by attracting customers with digital products and innovative services, many of which are made possible through machine learning and artificial intelligence.

Such players can easily meet customer demand as they already have lean and agile digital operations. To maintain leadership, incumbents must keep pace with the new normal of rapid disruption.

With its capacity to learn from large datasets and establish patterns and correlations, machine learning can revolutionize operations. It can inject new efficiencies into tasks such as risk assessment, fraud detection, anti-money-laundering, trading and customer service by providing instant insights, relevant recommendations and informed decisions in real time. However, it is important for organizations to establish a clear vision and strategy, ensure information is openly available and roll out change management programs to ensure successful machine-learning-powered transformation.
Machine learning refers to the use of mathematical and statistical models to teach machines about new phenomena. It involves ingesting raw information in large datasets, understanding patterns and correlations and drawing inferences. While this may seem similar to how humans learn, machine learning algorithms "learn" at much faster speeds with the ability to adapt from mistakes and course-correct.

Data security is a good example. Deep Instinct, a cybersecurity company that leverages deep learning for enterprise security, says new malware often contains code that is similar to previous versions. With this in mind, machine learning programs can easily identify typical user patterns and detect anomalous network behavior, making it easier to siphon resources into investigating legitimate cyberattacks.

Even in areas such as compliance, machine learning has the potential to infuse automation and run tasks that adhere to changing regulatory protocols with relative ease.

Applications of machine learning also include customer service. In 2014, CGI conducted a study to understand what banking customers want. According to its findings, 52% of customers wanted banks to show them how to save money and 55% wanted access to wealth-building advice by leveraging their financial data. Today, three years later, these demands are becoming a reality. Chatbots use machine learning to understand customer behavior, track spending patterns and tailor recommendations on how to manage finances. Erica, a Bank of America chatbot, helps customers perform routine banking transactions while offering simple insights on improving finance management. Machine learning also uses insights from customer data to curate targeted offers and promote relevant products and services, thereby increasing customer satisfaction.

Machine learning programs can identify anomalous actions for near-real-time fraud detection. The value of this in cost alone is significant considering that in 2016, customers lost nearly U.S. $16 billion in identity theft and online fraud. In banks, machine learning can establish patterns based on the historical behavior of account owners. When uncharacteristic transactions occur, an alert is generated indicating the possibility of fraud. Such predictive analytics can also be used as an anti-money-laundering tool to trace the true source of money by identifying disguised illegal cash flow, a tactic commonly used when laundering money.

When applied in contact centers, machine learning coupled with natural language processing and contextual engines can complement customer relationship management systems to resolve customer queries with relevant and real-time responses. This enhances the customer experience by offering personalized service at a lower cost — lower than the cost of training a human agent. Targeted and relevant communication about new products and services provides greater opportunities for cross-selling or upselling, thereby increasing customer satisfaction and driving greater business growth.

As an autonomous and self-learning technology, machine learning can be easily programmed to adhere to various regulatory protocols with minimal human supervision. This not only simplifies the complexity of meeting regulatory standards but also helps avoid penalties due to noncompliance.

Complex IT landscapes riddled with legacy systems pose several challenges when adopting new technologies. A collaborative study by the National Business Research Institute reveals that 12% of traditional financial institutions found AI to be "new, untested and risky" while others cited issues of regulatory compliance and disparate datasets as barriers to adoption of machine learning.

As a disruptive technology, machine learning and AI-driven solutions can transform banking as we know it. However, it pays to err on the side of caution. Here are some aspects to be considered before adopting machine learning.

Displaced workforce. According to the U.S. Bureau of Labor Statistics, automation will cause an 8% decline in the numbers of bank tellers between 2014 and 2024 and the number of insurance underwriters will drop by 11%. As machine learning replaces manual jobs, organizations must institute a plan of action on how to handle the displaced workforce. This may involve re-skilling programs and staffing workers in value-generating areas.

The right strategy. In a 2015 introductory article on machine learning, McKinsey warns organizations that, without the right strategy, machine learning will be relegated to routine applications that conduct repetitive tasks. The potential of machine learning transcends these boundaries when used correctly. To achieve this, there must buy-in from senior leadership with a strong vision and strategy on implementing machine learning. In fact, an article by PricewaterhouseCoopers recommends adopting AI in two different streams, one in operational areas to demonstrate the tangible benefits of AI and the other in emerging areas to demonstrate the value of innovative insights that humans cannot uncover. For instance, at an operational level, machine learning can replace back-office tasks pertaining to account opening demonstrating immediate benefits such as time, effort and cost savings. On an innovation level, machine learning can help discover unique metrics that can attribute accurate credit scores to individuals without a credit history by using other behavioral data.

Open information. For machine learning to succeed, it needs access to large datasets. This means organizations must make data freely available to machine learning software so they can ingest inputs and churn out accurate insights and predictions. However, care must be taken to create the right protocols when dealing with databases that contain sensitive and protected customer data to prevent violations.

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