By now, you’ve undoubtedly heard the terms “artificial intelligence” and “machine learning”.  If you haven’t already, take a quick read of our previous article where we explain the basics of machine learning.

Today, banks and credit unions are learning how to use the power of artificial intelligence (AI) to boost customer engagement, decrease costs, improve revenue, and pin-point fraud. AI is poised to truly revolutionize the way financial institutions gather information, harness data and interact with customers and members.

So, what exactly is AI?

AI all started out as science fiction: computers that can talk and think like humans.  Industry experts use the term artificial intelligence as an umbrella term that includes multiple technologies, such as machine learning, deep learning, and computer vision.  AI is the general field that covers everything that has anything to do with programming machines with “intelligence,” with the goal of emulating a human’s unique reasoning ability.  Think of AI as developing computer systems to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, translation between languages, and much more.

Uses Cases and The Power of AI

From Google’s development of the driverless cars to Skype’s launch of real-time voice translation, AI is now becoming an everyday reality that is changing aspects of our lives. To give you a better idea of how AI is becoming more prevalent and how it’s evolved, here’s a short list of popular AI use cases and some applications FI’s are widely embracing today:

Voice enabled assistants. Did you know the first tool enabled to perform digital speech recognition was the IBM Shoebox, presented at the 1962 World’s Fair? Today, everyone is familiar with voice assistants and other smart device voice technologies. As more and more people gain familiarity with voice assistance to quickly gather information, we will be seeing an increase in acceptance of and a rise in the demand for other applications that rely on voice enabled technology.  From healthcare to driving directions to workspace operations, this form of AI can make a significant impact on how businesses operate.

Financial services are a high-profile industry for voice assistance. In December 2017, Jack Henry’s Symitar® division introduced voice-enabled financial transactions to Amazon® Alexa®  through its Financial Innovations Voice Experience (FIVE) solution. Consumers can simply speak to Alexa to conduct a wide variety of transactions such as: check account balances, transfer funds, make payments, get loan payoff amounts, cancel payment cards, and more.

Smart Assistants: Smart assistants and home robots like Aido have come into the domestic scene. From assisted healthcare to automated customer service, consumers are experiencing the power of smart machines all the time. Even the Drone technology has been re-designed to accomplish tasks for you autonomously by a command on your smart phone.

The capabilities and usage of smart assistants is expanding rapidly, with new products entering the market. An online poll found the most widely used in the US were Apple’s Siri (34%), Google Assistant (19%), Amazon Alexa (6%), and Microsoft Cortana (4%).

Marketing Automation: Retailers and big brands are investing in the power of AI to further personalize and customize marketing emails based on customer preferences and behavior to engage them more and to prompt consumers to make a purchase. AI tools and software allow companies to send customized email newsletters based on previous interactions recipients have had with content to create a richer, more engaging brand image.

Risk Management: Fraud detection and risk management is an imperative focus for banks and credit unions. That’s why AI is being applied to fraud mitigation technology at a rapid pace. Through AI and algorithms, financial institutions are more effectively mining data to uncover suspicious activity and meaningful patterns, which then translates to information used to detect, spot, and mitigate fraud. Using AI to identify accounts, customers or transactions, for instance, that have unusual characteristics can expedite warning signs of abnormalities and verify suspicious activity that fraud is taking place.

Analytic Tools: Financial institutions realize they have a head start with the application of AI, since they have large data sets and experience with analytical tools.  Improving the customer experience is one of the greatest use cases for banks and credit unions since AI and advanced data analytics provides the opportunity for improved and faster decision making by deriving deep and actionable insights (e.g. customer behavior patterns). Some of these interactions will be with new voice or chatbot technology, while other applications will be behind the scenes, supporting marketing communication.

The use cases of AI are limitless—especially for financial institutions. AI helps us open our minds to how machines can help perform task more efficient and more accurate, while delivering greater overall results.

By partnering with right data analytic professionals, the power of AI and the insights it leads to can be realized faster, ultimately determining the financial institutions’ competitive differentiation in the future. If you have questions regarding AI or machine learning, contact The Knowlton Group today.

Sources:

Financial Brand:  How FI are Turing AI into ROI. Sept. 2017

USA Today: June 5, 2017: Apple Unveils $349 HomePod to bring voice to home audio

Dataversity: AI overview May 2017
Internet of Things: Tech Target

 

Explaining the Basics of Machine Learning

Financial institutions are no strangers to Machine Learning. Many institutions are investing heavily in this technology to improve cyber security, customer segmentation, and marketing campaign management.

To simplify the discussion, think of machine learning as self-driving cars, practical speech recognition, and effective web searches. Machine learning is the science of getting computers to act and learn from data without being explicitly programmed. Machine learning works with data and processes it to discover patterns that can be later used to analyze new data. Industry experts refer to machine learning as “training” that requires sending large amounts of data to the algorithm (a process or set of rules to be followed in calculations or other problem-solving operations) and allowing the algorithm to adjust itself and improve.

There is no way a human can look at large volumes of data and make sense out of it. Even if it is possible, the data would be peppered with errors. Precisely why machine learning is considered a significant technological development that is so widely used in everyday situations that you probably experience without even realizing it. For example, have you visited an online store and looked at a product but didn’t buy it — and then saw digital ads across the web for that exact product for days afterward? Has your credit card been declined while you were traveling or on vacation? You may have been on the receiving end of machine learning.

Machine Learning Use Cases

Machine learning’s capabilities are proving to be particularly useful in identifying patterns across large volumes of customer and user data and helping drive better outcomes. Here are a few use case examples of its impact on certain industries.

Fraud Detection: Machine learning is getting better and better at spotting potential cases of fraud across many different fields. PayPal, for example, is using machine learning to fight money laundering. PayPal uses tools that compare millions of transactions and can precisely distinguish between legitimate and fraudulent transactions between buyers and sellers.(1)

Automotive Industry: Self-driving vehicles could lead to a safer, cleaner, more efficient future for transportation. Software developers use machine learning algorithms to power computer vision that allows the vehicle to make decisions in ways that are similar to human decision making.(2)

Health Care: In computer-aided diagnosis (CAD), machine learning techniques have been widely applied to learn a hypothesis from diagnosed samples to assist medical experts in making a diagnosis. Machine learning has recently made headlines by helping to identify cancerous tumors on mammograms and to identify skin cancer. In a trade medical report, the results of a deep machine-learning algorithm helped diagnose diabetic retinopathy in retinal images. (3) Additionally, machine learning can be used to understand risk factors for disease and assist physicians by more effectively diagnosing and treating patients.

Machine Learning Then and Now

Machine learning continues to evolve and prove its worth in endless applications. Ten to five years ago, companies were limited in their ability to analyzing data sets. The technology to learn from massive data simply didn’t exist. Today, with fast computers and swaths of data sets, companies can analyze and learn things that are much more complex. Plus, the renewed interest in machine learning in recent years has exploded even more due to the amount of data companies collect, consume and compute has grown exponentially.

Beyond just a new technology buzzword, machine learning is reshaping many industries in a wide range of applications. Leveraging the insights, predictions and data-backed decision-making poses a tremendous opportunity for industries—especially for financial institutions. Through machine learning and the data insights produced, financial institutions can significantly improve business decisions and business outcomes—and machine learning will continue to refine and improve these outcomes over time.

Look for our next articles that explain artificial intelligence as we explore ways in which financial institutions can harness the power of AI and machine learning.

If you are interested in learning more about how your financial institution can best utilize machine learning applications, contact The Knowlton Group today. We are experts in data analytics and offer several services to enable your organization to become data-driven.

Sources:
1. Forbes, September 2016: Top 10 AI and Machine Learning Uses Cases Everyone Should Know About
2. IoT for All: November 2017
3. The JAMA Network Journal: Dec. 2016