Using Machine Learning To Turbocharge Financial Services Innovation

Toni Tomic

When people talk about artificial intelligence (AI), the discussion commonly turns to flashy robotics – how they support manufacturing production lines, disable explosives, or even vacuum the floors.

Major steps in AI were made in the late 1950s and early ‘60s, with flagship examples like the ELIZA computer program that demonstrated the superficiality of communication between humans and machines. From then until the 1980s, there was great promise that AI could revolutionize businesses, but there was no major disruption.

Today it feels as if AI is born again, and it is much more than robots. Innovative new AI technologies are delivering benefits to a wide variety of industries, including financial services.

According to the “Worldwide Semiannual Cognitive Artificial Intelligence Systems Spending Guide” from International Data Corporation (IDC), worldwide revenues for cognitive and artificial intelligence (AI) systems will reach $12.0 billion in 2017, an increase of 59.1% over 2016. “Cognitive and artificial intelligence solutions continue to proliferate across all industries, resulting in significant growth opportunities,” said Marianne Daquila, research manager, Customer Insights and Analysis, at IDC. “Some of the use cases are very industry specific, such as diagnosis and treatment in healthcare, and in others, they are common across multiple industries such as automated customer service agents. The variety, application, and nature of cognitive/artificial intelligence use cases are resulting in ubiquitous spend over the forecast period.”

One of the most interesting disciplines is machine learning, a specific type of AI that allows computers to learn without being explicitly programmed to do so. Machine learning uses statistical theory and exponentially more powerful computer processing to help businesses quickly realize valuable insight from their data.

This is great news for banks and insurers. Not only are these service providers facing falling profit margins, rising customer expectations, and increasing competition from fintechs, but they also need to cut costs. With machine learning, they can extract value from huge volumes of data, cheaply and effectively.

Machine learning can help traditional global banks that operate accounts at 140 to 170 British pound sterling (GBP) to compete better with challenger banks that operate at 4 to 44 GBP.

Creating intelligent financial services

Machine learning is ideal for addressing three dominant financial services challenges (see figure), including:

  • Customer front office: Unsupervised machine learning techniques can help banks and insurers segment their customers and offer personalized, targeted products. These technologies can also improve speed and agility, helping companies compete with fintech firms through enhanced knowledge of their customers.
  • Regulation and compliance: Using automated reports, stress-testing solutions, and behavioral analysis of e-mails and phone records to identify suspicious customer or employee behavior, machine learning can boost regulatory compliance. It can also enhance fraud detection, improve anti-money laundering efforts, and more effectively detect credit risk.
  • Operational efficiency: By combining Big Data with machine learning, financial services companies can automate back-office operations, reduce errors, and accelerate process execution. Insurers can improve and automate claims handling by recognizing patterns in pictures or individuals involved in damages, for example. Machine learning algorithms can also elevate talent management and recruitment by evaluating the resumes of successful employees while searching for online candidates with similar traits and experience.

Financial services challenges addressed by machine learning

1. Automating front-office applications

One of the most interesting machine learning applications helps financial services companies segment customers and offer targeted products or services. Let’s look at how the technology works.

Cluster analysis discovers distinct groups within the customer base and identifies similarities over several dimensions. Because this process is unsupervised, banks or insurers do not need to define the characteristics. The technology discovers these on its own.

Once the customer base is segmented, the technology builds predictive models. Algorithms help identify the most suitable products for each customer. And because the algorithms learn as they go, they can recognize changes in customer preferences in real time and automatically adjust product recommendations and provide the right advice at the right time.

The benefits can be significant. Personalized offerings make customers feel understood, increasing satisfaction. Successful cross-sell and upsell efforts can increase revenues. Service speed increases when banks and insurers can automatically recognize a change in behavior and respond instantly, without human intervention.

2. Choosing the right AI functionality

Traditional database technologies and analytics tools are not powerful enough to support machine learning. Fortunately, a new generation of innovative solutions is coming to market.

Many vendors are seeking to capitalize on this burgeoning industry. As with any new technology, decision-makers must carefully assess how well the tools meet the needs of the business.

When choosing a machine learning solution for customer retention, for example, we advise financial services companies to select tools that automatically:

  • Manage dynamic data from a variety of customer channels and build an overview of the customer journey
  • Sort, classify, and route events, pinpointing critical changes and reliable customer churn indicators
  • Identify customers who are about to churn and take proactive steps to prevent customers from leaving

In the battle to provide better customer service, machine learning technology is becoming a differentiating technology for financial services institutions. There are two ways to get there. Companies can:

  • Use standard applications, where machine learning is embedded and shipped with the software
  • Develop their own applications based on available cloud platform solutions and toolsets

Preparing to address obstacles

Without a doubt, there is huge potential to bring efficiency and effectiveness to a new level by injecting AI and machine learning into the financial services business. So what is hindering financial services institutions from becoming more successful with AI and machine learning and gaining benefits from disruptive technology?

There are three major obstacles:

  • Full senior management buy-in that extends beyond funding a proof of concept
  • Shortage of specialist skills to operate and maintain the technology
  • Costs of the AI system

With these challenges, getting quick wins in a short time frame is becoming more and more crucial. And since vendors are embedding machine learning scenarios into existing software applications, this might be a good starting point for companies that want to simultaneously work on major disruptive use cases.

Is your company ready to embrace the competitive advantages offered by AI and machine learning? To learn more about how machine learning can help financial services companies innovate and compete, contact me in the comments below or on LinkedIn.


Toni Tomic

About Toni Tomic

Toni Tomic is the Vice President and Global Head of Transformation at SAP. He is responsible for the SAP innovation strategy for disruptive technology such as artificial intelligence, blockchain, and the Internet of Things. Toni also oversees FinTechs and drives financial services business development globally. Before joining SAP, Toni worked for 10 years in strategy and management consulting focused on business and IT strategy, post-merger integration, and restructuring programs in the financial services industry.