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.   

DefinedCrowd Japan announces strategic partnership with Hmcomm

日本語版はこちら

We are delighted to announce our partnership with Hmcomm Inc, a leader in speech recognition technology in Japan and a fellow Sony-backed startup. A strategic move for both businesses, our joining poses great potential for the development of next-generation solutions in AI.

Hmcomm – whose name is derived from human-machine communication – develops innovative AI solutions in the field of automatic speech recognition (ASR). DefinedCrowd will be providing them with high-quality training data to take their AI initiatives to the next level. Our Founder and CEO expressed her enthusiasm about the joint venture:

“We are thrilled to partner with Hmcomm. It’s an exciting opportunity to collaborate with a fellow Sony-backed company that is a leader in speech recognition technology in Japan. We look forward to helping improve Hmcomm’s ASR model with high quality training data.”

Dr. Daniela Braga, Founder and CEO, DefinedCrowd

An AI startup spun out of the National Institute of Advanced Industrial Science and Technology (AIST), Hmcomm has developed ground-breaking solutions in their field, combining both dictionary model learning and emotional annotation processes. DefinedCrowd’s ability to provide efficient and reliable speech annotation through our unique combination of machine learning technology and human intelligence, means we’re perfectly positioned to deliver added value to Hmcomm’s work.

“We’re happy to announce our partnership with DefinedCrowd, a Sony investee company like us. To enable the social implementation of AI, access to a data collection platform with high-quality is key. I’m excited to collaborate with a global leader in AI data like DefinedCrowd that elevates our solution dramatically and rapidly.”

Koji Mitsumoto, CEO, Hmcomm

Our partnership is further endorsed by Mr. Jun Kitagawa, Senior Investment Manager of Sony’s Innovation Fund: “We sincerely look forward to the formation of a strategic partnership between the two startups that Sony has chosen to support. As experts in algorithms and training data technology, two fundamental pillars in the maximization of speech recognition accuracy through Machine Learning, the collaboration between these companies holds great potential. Both have shown rapid customer growth with outstanding results and we look forward to further promoting their work as they bring about revolutionary customer experiences through new and innovative technologies.”

Founded by a linguistics expert and with quality and global scale as two of our core values, this partnership is a perfect example of DefinedCrowd’s commitment to raising the bar on an international level. We couldn’t be more excited to see the results of our joint efforts with Hmcomm and look forward to working closely with our new partners in Japan.

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. 

Recapping the week at MWC19

Mobile World Congress (MWC) 2019, the world’s largest exhibition for the mobile industry, welcomed leaders from mobile operators, device manufacturers, technology providers, vendors and more.  

This year’s event saw a focus on two core concepts: 5G and Artificial Intelligence. It was said to be one of the most important events in recent times for the mobile industry. In the days leading up to the show, a warm buzz of anticipation filled the air as attendees were eager to hear about the new groundbreaking technologies. We were excited to be surrounded by leaders in the field and pleased to be a co-exhibitor for the Washington State Delegation of Commerce. 

With a large number of keynote presentations, panel discussion and exhibitors, there were many outtakes from the event. A hot topic that continued to emerge was AI bias. On day two I was able to discuss this topic with other like-minded people: Elena Fersman (Ericsson), Beena Ammanath (HPE), Beth Smith (IBM) and Kriti Sharma (Sage), who are all working towards an unbiased future for AI.  

We discussed ‘Democratizing AI and Attacking Algorithmic Bias’. The discussion of bias in AI continued throughout the event as many people came to speak with us about how to overcome this problem. If you missed this talk and want to hear more, see an edited version here.  

We also attended the Applied AI Forum: an exclusive conference that brought together telecom leaders, AI specialists, start-ups and academics, with an aim to spur debate and discussion on the practice of AI across the digital economy. Google and IBM Watson held an interesting panel discussion that explored ‘Applied AI: new trends and strategies’. In this forum, we were able to share lessons learned and discuss recent breakthroughs with both data scientists and global leads from several large enterprise companies.  

Another key highlight of MWC was our exciting hiring announcement! On the second day, we released our plans for the year: to double the size of our company by the end of 2019. With the rapid growth of AI applications seen across all industries, there is an increasing demand for high-quality data. And with this, our company is growing faster than ever. We are looking for more talent to join our team in Portugal, Japan, and the United States. See our careers page for more information.  

What a big week it was as we move into a new era of Intelligent Connectivity. A huge thanks to GSMA, a body representing the interests of mobile networks globally, and everyone we met at the event.  

We’d love to continue the discussions we had, especially around the topic on bias in AI. Reach out at pr@definedcrowd.com, we’d be glad to hear from you. We are already thinking about what next year might hold.   

Job description: talent for a smarter AI

We’re in a huge growth stage and are looking for talent to join our global team in Portugal, Japan, and the United States. Check out our careers page for current openings.

We accelerate the evolution of Artificial Intelligence initiatives by delivering high-quality training data to enterprise companies. We are investing heavily in our business and the people to make this happen. Over the coming months, we’ll be searching for professionals who are looking to embark on an exciting career while making a difference in AI.  

So, who are we? We’re an 80-employee startup based in Seattle, with offices in Lisbon, Porto, and Tokyo. Our CEO, Daniela Braga founded DefinedCrowd in 2015 to fill a gap in the market by offering high-quality training data to help machine learning products reach the market at optimal quality and speed. And with the rapid growth of AI applications and the high demand for this data, our business is growing so quickly that we are looking to nearly double our team by the end of 2019.

“We have a very ambitious goal –  to be the number one provider of data for AI in the world. This year will be crucial to achieving this goal, as we mature our product, grow our client base, and increase our partnerships with the companies that are leading the AI revolution.”

Founder and CEO, Dr. Daniela Braga

We are currently hiring positions in the following departments: Development, Product, Marketing, and Operations, with several openings for Software Developers (Frontend, Backend, and Full Stack), QA Automation Engineers, and Machine Learning Engineers. These positions are available within our four offices and will have an important role in the expansion of the company’s product: an all-in-one data platform.  

Earlier this year, DefinedCrowd was selected as one of CB Insights top 100 AI startups. Our client list includes many Fortune 500 companies including BMW, Mastercard, Nuance, and Yahoo Japan. We also have partnerships with IBM, Microsoft, and Amazon.  

“We are looking for the best talent to join our team in this exciting moment, and to be part of the construction of a smarter AI”

Founder and CEO, Dr. Daniela Braga

For anyone interested, make sure you keep an eye out on our careers page http://careers.definedcrowd.com, where there will be new jobs added throughout the year.  

5 trends in AI for 2019

It´s not easy to project trends in a market evolving as rapidly as AI. However, through analysis of cross-industry data and experience with a diverse client-base, we’re willing to make some bets. From automating mundane daily tasks to leveraging computer vision for more accurate medical diagnoses, here are 5 trends in AI we expect to emerge in 2019. 

TREND 1: “EDGY” AI 

Edge AI refers to processing AI algorithms locally instead of relying on cloud services or data centers. 

Smartphones, cars, and wearable devices are examples of devices that need to make faster and more accurate real-time decisions. Autonomous vehicles, for instance, need to make hundreds of decisions per second – brake, accelerate, turn on lights, identify and interpret traffic patterns, signals, and speed limits – all while simultaneously responding to the driver’s voice commands. These decisions must take place in a fraction of a second, and they need to be independent of the connectivity issues that come with cloud computing.  This means that autonomous vehicles need powerful chips to process all this information rapidly and accurately.  

Tech leaders like Nvidia, Qualcomm, Apple, AMD, and ARM are investing in developing and delivering chips that can handle these kinds of workloads. 

In 2019 we’ll see more models being deployed at the edge as well as specialized chips allowing AI models to operate independently from the centralized cloud, or on the “edge” if you prefer.

TREND 2: AI IN HEALTHCARE 

 Last year the FDA (U.S. Food and Drug Administration) approved IDx-DR, an AI-enabled software that can independently diagnose diabetic retinopathy before severe complications (such as blindness) emerge.  

The FDA also cleared Dip.io, a product developed by startup Healthy.io, as a class II medical device. This diagnostic tool can monitor urinary tract infections and track pregnancy-related complications by analyzing photos of dipstick urine tests. It’s as simple as uploading a photo, the model takes it from there.   

2019 will be a remarkable year for AI in healthcare. 

TREND 3: PREDICTIVE MAINTENANCE 

Equipment failure is one of the main causes of production downtime, a huge line-item for any asset-intensive business. However, today maintenance teams spend 80% of their time collecting data but only 20% analyzing it.  

Factory and field equipment generate mountains of unleveraged data that could go a long way to solving these issues. Alongside cameras and sensors, ML-driven algorithms can learn to check assets’ “vital signs,” catch small irregularities (a loose screw) before they turn into larger ones (a damaged turbine) and provide productivity predictions, allowing firms to plan accordingly.      

With sensors becoming more affordable, and edge computing gaining momentum, machine learning will become even more heavily incorporated in industrial processes in 2019. 

TREND 4: CONVERSATIONAL AI 

We say conversational AI, what pops into your head?  If it’s chatbots, you’re not alone. While that’s certainly a huge part, the technology is much broader as it is integrated across messaging apps and voice-enabled virtual assistants who go far beyond the scope of chatbots.    

In 2019 we can expect to see even more AI deployed to handle routine customer service interactions. Whether you’re booking a flight, searching for a new restaurant or requesting the arrival date of your next purchase, AI can assist you.   

Research from eMarketer shows that this year 66.6 million Americans are expected to use speech or voice recognition technology. Banking and retail are great examples of industries already using conversational AI initiatives, and as the technology continues to mature in 2019, we expect to see even more use cases in even more industries.

TREND 5: RPA / BACK OFFICE AUTOMATION 

RPA (Robot Process Automation) covers a variety of back-office tasks that can be automated by bots. It’s not a new concept, nor is it AI. But here are some interesting facts:   

  • According to McKinsey, RPA will have an economic impact of around $6.7 trillion by 2025.   
  • Forrester Research also mentioned that RPA market is estimated to grow to $2.1 billion by 2021.  

Although RPA is not considered AI – since it’s rule-based and can’t learn anything on its own – there’s been a collaboration between RPA and AI.  Due to its capacity of automating repetitive and time-consuming tasks, RPA can save employees tons of time, at the same time it can ensure processes are running smoothly and precisely. On the other hand, AI can enhance RPA.  

For instance, take a bank that’s onboarding a new client and needs to adhere to Know Your Customer/Anti Money Laundering Compliance Regulation. RPA is great for doing a lot of manual work. What AI can do is analyze the data the RPA’s pull in a more sophisticated manner, and arm a Compliance officer with more useful information.  

Whether there is a need to automate processes or implement solutions in this field, RPA has been mainly leveraged by large companies – until now. In 2019 we can expect to see small and medium-size businesses starting to adopt RPA, due to its clear benefits and increased popularity.  

Daniela Braga’s journey with DefinedCrowd

Earlier this week, DefinedCrowd was Featured in Jornal Económico, a premium financial publication in Portugal. We’ve translated the article from the original Portuguese for our English- speaking friends. Enjoy!

Original Article by
António Sarmento

Founded in Seattle, USA, DefinedCrowd is a startup specializing in training data for Artificial Intelligence. The company counts Amazon, IBM, and EDP as investors and clients. 

DefinedCrowd provides services so that data scientists can gather, structure, and enrich datasets for Artificial Intelligence, helping companies improve speed to market and the overall quality of their AI products. DefinedCrowd accelerates enterprise AI initiatives by combining machine learning technology with human-in-the-loop collection processes. Founded in August 2015 by entrepreneur Daniela Braga, the company is headquartered in Seattle, has R&D centers in Lisbon and Porto, and a sales office in Tokyo. 

Three months after its founding, the company opened their first R&D office at Startup Lisbon. Since then, DefinedCrowd has blossomed from an initial team of three employees to a workforce of more than 70 that is still growing.

In 2016, the company raised $ 1.1 million in seed funding, with investors such as Sony, Amazon Alexa Fund, Portugal Ventures, and Busy Angels.
In July 2018, DefinedCrowd closed a Series A funding worth $11.8 million, led by Evolution Equity Partners. EDP Ventures, Mastercard and Kibo Ventures joined as new investors, while Sony, Amazon, Portugal Ventures and Busy Angels bolstered their investments with additional capital for the data company.

“It is important to raise capital if we want to move fast, especially in the technological sector.”   

Daniela Braga to Jornal Económico

This influx of capital is being used to accelerate product development and accelerate team growth. Two-thirds of DefinedCrowd’s 70 employees work out of Portugal. The company expects to add 80 more team members by the end of 2019.

Over the past six months, DefinedCrowd has announced three partnerships: a formal designation as an Amazon Alexa Skills partner, a product integration with IBM Watson Studio; and participation as a featured vendor in Microsoft‘s co-sell program.

DefinedCrowd’s platform provides industry-agnostic data services and can support text, audio, and image annotation. The company’s clients span industries as a result: from Fintech, to Retail, Healthcare, Utilities, and the Internet of Things. Their client portfolio consists mostly of Fortune 500 companies, including BMW, MasterCard, EDP, José de Mello Saúde, SoftBank, Yahoo Japan, Randstad, and Nuance

DefinedCrowd’s goals are ambitious. The company aims to become the world’s number one AI data provider through expanding their client-base and forging new partnerships with industry leaders. 

With a degree in Portuguese Language and Literature, Daniela Braga has spent her career examining the rigorous use of language, the perfect foundation for her business. “We deal daily with data in 70 languages and dialects. Our clients need, at a minimum, native-level speakers and sometimes even require linguists or specialists in language sciences for all of them” says the entrepreneur.

After graduating with a master’s degree in applied linguistics, she went on to earn a PhD in Speech Technologies at the Faculty of Engineering at the University of Porto and taught at the University of A Coruña for two years before joining Microsoft (whom she worked for in Portugal, China and the United States).

After leaving Microsoft in 2013, Daniela moved to American company Voicebox. Simultaneously, she was invited to teach Data and Crowdsourcing for Speech Technologies at the University of Washington. It was during this time that she saw the gap between the Artificial Intelligence data scientists wanted to develop and the training data available to build it. She decided to found her own company as a result.

Waving a well-paid job goodbye, and with few personal resources, she started meeting with investors in Seattle, and quickly received an initial check: $ 200,000 in financing to start her business. A business that is now signing contracts with some of the largest companies in the world.

DefinedCrowd is in constant growth and employee numbers have been updated to reflect our current position.