AI in Banking: 3 use cases

In the age of technological and digital disruption, AI is showing rapid advancements across almost every industry. And with this, real-world business solutions are being vigorously adopted especially in the banking sector with one-third of banking CIO’s actively planning an AI project, highest among all industries.   

The highly competitive banking sector is seeing some of the most transformative effects of AI, with mostly larger banks such as Wells Fargo, JP Morgan, Bank of America, Citibank putting it to work across key areas of their business operations. Analysts predict that if AI is properly deployed, it has the potential to reduce banks’ costs by 25% and increase revenues by 30% within 5 to 7 years. AI fits extremely naturally with banking as it thrives on data. And as banks deal with enormous amounts of data, these technologies can transform all aspects of how banks work, from how they operate on the backend, to how they interact internally and externally.

So, what main concerns is AI addressing, and what AI-driven applications are being used to tackle them? Here are 3 ways AI is showing global traction in the banking industry: 

Customer Service 

Alongside new technology comes new ways of communicating, and these days it’s common to stumble across a voice or chatbot that delivers a surprisingly personalized customer service. And with the growing availability of choice when it comes to financial institutions, it’s more and more critical for banks to deliver excellent customer service on-demand to build loyalty.

Chatbots, interactive voice response (IVR) and virtual assistants are popular AI-enabled tools. And as the capabilities of AI such as natural language processing and speech recognition increase, banks will continue to adopt these solutions. Banks are not only employing these solutions to minimize costs, by up to 30%, but also to reduce end-to-end communication time with clients. For routine inquiries, bots are shown to improve response times by 99%, reducing time-to-resolution from hours to just a few minutes. The end result? A happier customer, faster. 

Royal Bank of Canada’s (RBC) NOMI is a great example of an AI-driven virtual assistant that is improving overall customer experience. The assistant responds to customers’ requests and queries and also provides other support features, such as: informing about available funds, alerting to anomalies or unusual activity and providing personalized insights and advice on financial management. Results from NOMI show not only increased usage of the banks’ mobile app and opening of savings accounts by 20%; but also a wealth of invaluable insights into their customer base.  

While not all banks are introducing virtual assistants to help with the multitude of customer demands, chatbots are a common and more simplified option, helping with everyday requests and decreasing response time. Other banks who have similarly implemented virtual assistants and chatbots include Bank of America, with Erica, and Wells Fargo has been piloting an AI-driven chatbot through Facebook messenger, both delivering a highly personalized customer service.

Process Optimization 

A key solution provided by AI-powered tools is process optimization. And a valuable use case in banking is using AI to enhance robotic process automation (RPA), the process in which software mimics human actions rather than AI which simulates human intelligence. When these two technologies are implemented together, the result is powerful: AI enables RPA to perform more complex automation such as interpreting, decision-making, and analyzing across various processes. The big benefit? It gives back time, reducing employees’ hours spent on mundane and repetitive tasks, and allows for more focus on high-value projects. 

Banking is among one of the biggest adopters of these initiatives and there are several applications being used to transform departments. A great example of a company using AI to optimize processes is American bank, JP Morgan. Their internal IT team use bots to respond to requests such as changing an employee’s password. With over 1.7 million minor requests year on year, these bots are highly valued especially for one of the largest banks in the US. 

JP Morgan has also launched a program called COiN (short for Contract Intelligence). The system reduces the time to review documents and has also proven to limit human error that occurs in loan servicing. Prior to the implementation of COiN, JP Morgan would review 12,000 commercial credit agreements taking nearly 360,000 hours. When dealing with large amounts of documents, mistakes could often arise; but now, thanks to their machine learning system, this task can be completed with a higher performance rate and in a matter of seconds.      

AI has shown tremendous potential to increase process optimization. Banks are already seeing successful outcomes, moving their employees’ time from small insignificant tasks to more valuable opportunities, essentially bringing more critical thinking into banking businesses. Not to mention, a more engaged and motivated workforce.  

Compliance and Risk Management 

Keeping up with the challenging environment of banking compliance and risk management is not only time consuming but also costly. And with the average bank spending $120 million annually on compliance and customer onboarding procedures, as well as tackling the increased frequency and complexity of cyber-attacks, there is enormous potential for AI technologies to support this area.

Banks need to respond to large amounts of unstructured data that emerge from difficult regulatory demands. AI has proven particularly effective in dealing with this data in daily tasks such as automating legal, compliance and risk documentation, as well as analyzing data sets that train machine learning algorithms to track credit card fraud or money laundering. A lot of these tasks involve excessive manual work; by moving them to an AI-powered system instead, banks can free up employees to deal with more complex decisions.   

Global financial group, Citibank, partnered with data science company, Feedzai, leaders in the market for real-time risk management in banking, to implement a transaction monitoring platform. Powered by machine learning technology, the system adjusts automatically to monitor discrepancies and changes in payment behaviors, thus enabling banks to manage risk and keep their customers safe from fraudsters.

Compliance and risk management has always been an important focus area for banking, and thanks to AI, there have been game changing developments. As AI continues to make considerable inroads in these areas, banks will be able to focus on more analytics, rather than spending their time avoiding risk or dealing with increased compliance regulations.   

Beyond the hype, AI is showing clear development with ample use cases and substantial return. And as banks continue to fight for customer loyalty, having the right technical solutions on the backend will be key to sustaining a competitive advantage. With AI use cases starting to appear from leading banks, others soon will follow suit. Over the next few years, we can expect to see further widespread adoption of AI in banking, and from not just the bigger players.   

How AI can help to understand the customer

Ahead of us is a significant change in the way brands use customer experience (CX).  We are already starting to see the switch from companies competing on price and product to competing on CX. But what exactly do we mean by CX? Gartner defines CX as a customer’s perceptions and feelings caused by the one-off and cumulative effect of interactions with a supplier’s employees, systems, channels or products.   

Previously, the communication flow between customers and companies was either in person, writing or via a telephone call to the support line. Now, there are increasingly more ways customers can interact with brands, and when they do, they expect a high-quality experience “on demand.” 81% of marketing leaders were expected to mostly or completely compete based on customer experience by 2019, as revealed in the 2017 Gartner Customer Experience in Marketing Survey.  

There are many tools already giving insight to CX, such as NPS and Customer Success Scores. However, when companies need to make quick decisions, real-time insights are what’s helping decision makers. Technologies such as AI are now gathering these insights by allowing companies to organize and categorize data based on business needs, helping to make sense of all these interactions.  

To understand the customer from a CX perspective, and give some real-world examples, we can filter down a myriad of AI technologies and categorize them into three buckets: 

  • Speech Analytics: understanding, interpreting and analyzing voice conversations. Example: understand sentiment, IVR systems.
  • Image: capturing, processing and analyzing images, photos and video. Example: customer patterns, social media image analysis. 
  • Natural Language Processing: analyzing human expression and emotion. Example: text, chatbot, email analysis.  

The below table shows CX use cases and examples of these AI technologies in action:  

Source: Gartner 2019

Are data scientists the only ones needing to understand these technologies? No, it’s extremely valuable to both marketing and CX teams to gain an understanding of these tools. Every company has unique needs depending on CX goals and business objectives. Teams need to make a well-informed decision and understand which tools are most useful to their business, which will essentially lead to more accurate decision-making and a customer-first approach.     

Now, are people rushing to adopt these new AI technologies for CX? In Gartner´s 2018 Enterprise AI survey, it was revealed that businesses that are already deploying AI, 26% are implementing it to improve customer experience. Although it may not seem urgent to start implementing these technologies right away, it’s important that businesses are aware and start to familiarize themselves with these AI applications.  

A good place to start is mapping out a customer journey and finding the ‘dark spots’. These are the areas that could benefit from deeper real-time insights, such as understanding the mood of a customer when they are talking with a chatbot. Having these insights will allow you to hand over the conversation to a human based on the customer’s emotion.  

Companies are dealing with an increasing number of interactions happening across multiple channels and devices. With customer expectations are at an all-time high, it’s not easy to connect all these touch points and deliver an excellent customer. AI can help provide rich insights allowing you to get faster, real-time understandings, and optimize the overall customer journey. 

The Machine Learning Lover’s Holiday Book List

In the market for some last-minute gift recommendations for a machine learning “geek?” (we use the term affectionately around here). DefinedCrowd’s got you covered with our “machine learning lover’s book list,” hand-selected by our ML Team. From the ins and outs of speech and language processing to broad-level theoretical overviews of the machine learning field, these texts cover the wide-ranging topics we discuss in our office every day. Enjoy! And happy holidays from all of us at DefinedCrowd.

Speech and Language Processing An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition by Daniel Jurafsky

Summary: In this excellent intro to speech and language processing tecnologies, Jurafsky presents an empirical approach that comprehensively covers language technology based on applying statistical and machine-learning algorithms alongside modern technologies. The book largely emphasizes scientific evaluation and practical applications.

Foundations of Statistical Natural Language Processing by Christopher D. Manning and Hinrich Schütze

Summary:
A fantastic introduction to statistical natural language processing that uses an analytical approach to cover a range of mathematical and linguistic foundations for NLP technologies. Foundations of Statistical Natural Language Processing proves further useful in presenting theoretical and algorithmic building blocks for NLP technologies.

Crowdsourcing for Speech Processing: Applications to Data Collection, Transcription and Assessment by Maxine Eskenazi, Gina-Anne Levow, Helen Meng, Gabriel Parent and David Suendermann

Summary: An essential read for anyone interested in learning more about crowdsourcing training data for speech models. Offers a comprehensive overview from the basics of setting up a task, to tips for task interfaces and methodologies for quality assessment.

Deep Learning (Adaptive Computation and Machine Learning series) by Ian Goodfellow, Yoshua Bengio, Aaron Courville and Francis Bach

Summary:
This book offers a great introduction to what many consider the “Holy Grail” of Machine Learning.
Topics covered, range from mathematical and conceptual background to deep learning techniques. The “research perspectives” that book-end chapters with specific case-studies make Deep Learning a great resource for students and software engineers alike.

Deep Learning for Computer Vison with Python by Adrian Rosebrock

Summary: For those looking to master deep learning for image recognition and classification, Deep Learning for Computer Vision offers practical walk-throughs, hands-on tutorials and a direct teaching style. Useful for both beginners and for the seasoned deep learning pro looking to brush up on the fundamentals.