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Artificial Intelligence, Financial Services

Security and compliance mean that the financial industry tends to be slower to adopt new technologies compared to other, less regulated industries. However, artificial intelligence is starting to take hold in a big way.

While AI has been around for a long time, it is now becoming an everyday part of how we bank, invest, and get insured. According to Forbes, more than $4 billion in newly funded ventures focused on financial services AI applications in the last two years. Adoption is set to increase as more sophisticated technologies come to market with over 30% of financial services companies already leveraging AI in some capacity. In March, Accenture released a report that stated that 76% of banking executives intend to deploy AI within the next three years.

Financial institutions have leaped into AI by exploring various areas of their business where artificial intelligence can be applied with their focus on decreasing costs, enhancing revenue, reducing fraud, and improving customer experience.

Deloitte recently released a white paper titled AI and You: Perceptions of Artificial Intelligence from the EMEA Financial Services Industry. According to the paper, AI can be described in terms of three application domains: cognitive automation, cognitive engagement, and cognitive insight.

  • Cognitive automation: Machine learning (ML), Robotics Process Automation (RPA), natural language processing (NLP) and other cognitive tools develop deep domain-specific expertise and then automate related tasks.
  • Cognitive engagement: Systems that employ cognitive technology to engage with people, unlocking the power of unstructured data (industry reports / financial news) leveraging text/image/video understanding, offering a personalized engagement between banks and customers with personalized product offerings and unlocking new revenue streams.
  • Cognitive insights: Cognitive Insights refer to the extraction of concepts and relationships from various data streams to generate personalized and relevant answers hidden within a mass of structured and unstructured data. Cognitive Insights allow detecting of real-time key patterns and relationships from a large amount of data across multiple sources to derive deep and actionable insights.

Have you noticed a change in the way you bank? Here are some of the ways financial services companies are leveraging artificial intelligence.

Online banking has made it more convenient for customers to check their balances, transfer money, pay bills, etc. but what about when you need help with something? Many financial services companies have started experimenting with chatbots. This replaces the need for humans to answer repetitive or straightforward questions like helping someone reset their password.

These text-based chat bots are just the beginning. Some banks are experimenting with upgrades to this automated service by leveraging predictive analytics and cognitive technologies to provide customers with highly personalized support. The virtual assistants will one day be able to access all of your financial information to help you make the best financial decisions based on your current situation and future goals without ever stepping foot inside a bank or talking to someone on the phone.

To make the best financial decisions, most times you need to analyze copious amounts of data. UBS is leveraging machine learning to scan large amounts of trading data to determine the best trading strategy in what they are calling ‘intelligent automation.’ While humans are still required to look over and approve the strategy, this technology upgrade increases the rate at which informed decisions can be made.

Human error can be very costly for financial institutions. To cut down on loan-servicing mistakes, JP Morgan launched its COIN (contract intelligence) program. This machine learning technology is used to review and interpret commercial loan agreements. This new technology is capable of cutting an estimated 360,000 hours of human work done by lawyers and financial loan officers.

Are you working on AI-powered FinTech? Check out how iMerit’s team of in-house data experts can help you with your data needs.



Have you ever found a photo and weren’t sure where it was taken? Wondered what certain parts of the world look like? Needed to monitor changes in landscape over time?

These are some big problems that AI and Deep Learning are helping to solve in the geospatial industry. Some believe that Geospatial is the golden thread that links many of the large datasets and is at the heart of making sense of them.

By classifying, tagging, labeling maps and satellite images, companies in the geospatial industry can leverage machine learning to track and identify various interests. Here are some cool companies doing innovative things in Geospatial AI. keep reading


computer vision

Last week, the iMerit team was fortunate enough to sponsor and attend the O’Reilly Artificial Intelligence Conference in San Francisco. The sold-out event brought together business leaders and AI innovators from across the industry to discuss where AI is today and where applied AI is headed.

This conference was one of the highlights of our year so far. It stands out for the lineup of speakers, great vendors, and engaged audience from across all fields, from logistics and manufacturing to health and media. The conference featured cutting-edge science and business implementation, focusing on topics like increased AI accessibility, innovations in AI techniques, and how AI is driving a paradigm shift in computing itself.

keep reading

computer vision

From aerial drones and cloud computing to augmented reality and virtual assistants, the tech world is awash with new developments that feel like they’ve been taken from the pages of a science fiction novel. In many cases, these revolutionary technologies seem to obviate the need for human beings, letting you enjoy the benefits without […]

From aerial drones and cloud computing to augmented reality and virtual assistants, the tech world is awash with new developments that feel like they’ve been taken from the pages of a science fiction novel. In many cases, these revolutionary technologies seem to obviate the need for human beings, letting you enjoy the benefits without having to lift a finger.

Along with these powerful innovations comes the fear of being replaced by a machine. However, it’s all too easy to forget that such innovations didn’t just come out of thin air. In most cases, humans are still required in order to bring the magic behind these inventions to life, training the algorithms that power these new technologies. Here’s a look at how humans are having an impact on some of the biggest tech trends of today and tomorrow.

Self-Driving Cars

self-driving car
Few inventions capture the promise and wonder of futuristic technologies like self-driving cars. Companies like Uber are already looking for ways to automate their fleet of vehicles, removing the need for a driver entirely, with other tech titans like Google and Tesla also throwing their hat in the ring.

For these cars to drive on their own, they need ‘vision.’ To train them to recognize various objects on the road, they need to be fed information. Humans annotate and/or segment thousands (sometimes Millions) of images of streetscapes to train the computer vision’s algorithm to learn to recognize what is a road versus a sidewalk, or a pedestrian versus a cyclist and how to prioritize them in decision making. [This enhances the safety quotient of these vehicles]

Humans also play a large role in keeping these vehicles on the road. When automotive tech company Delphi made a nine-day cross-country road trip from San Francisco to New York City with a self-driving car, the vehicle was able to navigate on its own 99 percent of the time — but the engineers along for the ride still had to steer occasionally in order to handle anomalies like construction zones and aggressive lane changes.

IBM Watson

IBM’s supercomputer Watson first burst onto the scene in 2011, when it beat “Jeopardy!” champions Ken Jennings and Brad Rutter at their own game. Currently, the technology is used for a variety of commercial applications, from diagnosing illnesses to improving business processes.

Of course, in order to get on “Jeopardy!” in the first place, Watson had to be able to comprehend a variety of texts in English. To do so, Watson relied on developments in natural language processing such as named entity recognition and coreference resolution in order to resolve potential ambiguities. Once Watson understood the question, it searched through a locally-stored database of 200 million pages of information for the correct answer.
Watson owes its game show wins — and its successes in other fields — to an array of countless human workers who train it and help improve it. Human Experts work to improve Watson through curating content, building training datasets for machine learning, In order for Watson to read doctors’ handwriting, for example, human typists had to painstakingly enter thousands and thousands of texts, and then match them with the correct images for Watson to examine. There is constant and ongoing communication between Watson and humans improve accuracy and remain up to date.

Snapchat Filters

Sure, self-driving cars and talking robots are cool — but we don’t use these in everyday life yet. Snapchat “lenses” (popularly known as filters) are one of the app’s defining features and a massive hit on social media, letting you take pictures where you’re wearing a flower crown or swapping faces with a friend.

In order to bring these filters to life, however, the Snapchat app first has to determine where your facial features are located using computer vision so that it can properly impose another image on your head. According to company patents, Snapchat uses an “active shape model” of an average human face that’s been trained by feeding their algorithms thousands of images that have been annotated to identify key facial features. It then applies this model to your face, adjusting it where necessary in the case of deviations.


Next time you see a really cool technology innovation, remember the human intelligence that went into building it and maintaining it.