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Considered the next big technological shift, artificial intelligence (AI) refers to systems that change behaviors without being explicitly programmed, leveraging aggregated data, usage analysis, pattern recognition and predictive analytics. AI is not one universal technology, rather, it’s an umbrella term that includes multiple technologies such as machine learning, deep learning, computer vision, and natural language processing (NLP). These technologies work, both individually and in combination, to add intelligence to applications.

While popular culture has embraced AI as shorthand for artificial intelligence, at Microsoft, we believe the term “augmented intelligence” is more accurate. It is an alternative conceptualization that focuses on AI’s assistive role, serving to enhance human intelligence rather than replace it. As Microsoft CEO Satya Nadella has said, “Humans and machines will work together—not against one another. By augmenting human capability, AI allows people to move into activities where they can add more value, making them more efficient.”

A powerful set of tools for scenarios in banking

In the context of financial services, AI provides banks with an enormously powerful set of tools to transform and streamline some of their most fundamental financial processes. The challenge for many organizations, however, is less about identifying and adopting the best AI technologies, than in reshaping and rethinking their operating models and talent development to take advantage of AI’s transformative capabilities. AI can help financial institutions dramatically improve operational efficiency and gain a much clearer understanding of their business and their customers. It is still up to humans to make the big strategic decisions and set the course for AI and related technologies to help deliver profitable growth.

There are many financial services-specific scenarios where AI can add significant value, for example:

  • Reducing false positives in anti-money laundering (AML) and sanction-screening alerts. AI can be used to monitor all activity within your financial institution from a global view and link common “bad actors” together. This drastically reduces false positive alerts without compromising compliance with regulatory guidelines.
  • Optimizing management of non-performing loans (NPLs). AI can streamline forecasting, flag corrective actions, and automate communications to customers.
  • Advisory services augmented with robo-advisors. More and more investors are turning to robo-advisors for essential investment needs due to their convenience, ease of use, affordability, and transparency. They can provide a range of advisory services, from personalized, automated, algorithm-based portfolio management, to sophisticated tax strategies and risk management, at markedly lower costs than traditional advisory models.
  • Digital onboarding. AI can enhance the experience of adopting a new product or service by assisting customers all along the way in the onboarding process, detecting emotional response, and making use of optical recognition to automate information capture.

Note: for more examples of AI applications in the financial services sector, visit the Cortana Intelligence Gallery.

Getting started on the AI journey

Even if you are clear about which scenarios you wish to enable and augment within your business, you may still be grappling with the challenge of where to start. Whatever scenario you have in mind, here is a suggested set of steps you can take to get started on your journey of adopting AI:

  1. Develop an AI strategy aligned with your overall business strategy. Decide which businesses to disrupt and which to enhance. Determine where AI can assist in relation to the organization’s top priorities.
  2. Democratize AI and make it an enterprise-wide capability. Evaluate the current AI skills within your organization. Recruit data scientists and other professionals who understand AI. Consider using a partner such as Microsoft.
  3. Establish appropriate governance. Develop clear policies around data privacy, decision rights, autonomy, and transparency. (It is worth noting that ethics regarding privacy and accuracy of AI technologies are in early development; Microsoft is participating with other industry leaders in defining new standards to match the challenges posed by the technology.)
  4. Put your data house in order. Get all your data together in a data lake: centralized access to data is crucial to enabling the value that AI can offer.
  5. Find the right mix of solutions. Embrace cloud platforms and find the right mix of cloud and on-premises tools. Most banks will be better off using a cloud-based AI platform rather than building their own, especially if they wish to benefit from the scale and speed afforded by the cloud.
  6. Start with a clearly defined proof of business value. Start with the business outcome you want to achieve—whether improving the responsiveness of customer service or reducing labor costs—and then consider whether AI is the right solution for getting you there. Follow a design-led approach and seek ways to help humans; regularly track progress and adjust accordingly.

While AI might seem like a far-off development, when understood as augmentative to, rather than a replacement of, current business operations, it is already offering considerable value to the financial services industry. The question now is less a matter of where AI can be applied, but rather where can AI deliver the most strategic value. And as with most things, getting started is at least half the battle. To this end, a knowledgeable technology partner can be a critical asset in defining the right strategy for AI adoption and developing a roadmap for business transformation.


At Microsoft, our approach applies technology in unique ways—with a trusted cloud platform, tools, and services that deliver augmented intelligence capabilities and empower business agility. As your trusted technology partner, we offer both industry know-how and enterprise-grade solutions. We can help no matter where you are on your digital transformation roadmap.