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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.

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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.

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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.

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Blog, eCommerce

The hyper-competitiveness of the e-commerce industry has turned it into one of the biggest drivers of technology innovation. Today, the need for differentiation and the pressure to discount is driving e-commerce companies to look more closely at big data innovations such as predictive and prescriptive analytics, as well as artificial intelligence solutions, to remain competitive. The challenge lies in vast amounts of data. Big data is providing the opportunity for […]

The hyper-competitiveness of the e-commerce industry has turned it into one of the biggest drivers of technology innovation. Today, the need for differentiation and the pressure to discount is driving e-commerce companies to look more closely at big data innovations such as predictive and prescriptive analytics, as well as artificial intelligence solutions, to remain competitive.

The challenge lies in vast amounts of data. Big data is providing the opportunity for innovation, but its abundance and unstructured nature are inhibiting progress. To be data-driven, you first need to make sense of the data. For machines to read data, it first needs to be in a structured format. Human computing is unlocking opportunities hidden within a fog of data.

Big Data as a Driving Force in Innovation

Some of the most important innovations that have revolutionized the e-commerce industry have come from big data. eBay, a global leader in personalization, is a great example. Big data serves as a tool to drive innovation in customer experience with its ability to recommend similar items to a shopping guest. Also, big data powers eBay’s predictive machine learning algorithms that detect fraud based on purchase history and take action based on learnings within the data.

The eCommerce industry has access to substantially more data than many other industries. From CRM data and purchase history to social, website traffic and cookie data, eCommerce has an abundance of data to help them plan their next strategic move.

What Big Data Can Do For e-Commerce

The abundance of data that we now have access to gives retailers the opportunity to provide guests with a personalized experience and improve business operation efficiencies. According to Jeff Bezos, big data is powering the core functions of e-commerce businesses today.

“Machine learning drives our algorithms for demand forecasting, product search ranking, product and deals recommendations and much more. Though less visible, much of the impact of machine learning will be core of this type – quietly but meaningfully improving core operations.” – Jeff Bezos, Amazon CEO

For example, when TeeSpring, a custom apparel company, was looking to make it easier for their guests to find the right t-shirt design for them, they knew that with better data, they could offer their customers a better experience. For a guest to find exactly what they are looking for (or the perfect things they didn’t know they needed), TeeSpring knew that each graphic would need to be tagged with descriptive terms to ensure it showed up in related searches. Enhancing the search functionality with better, more accurate data helps TeeSpring offer a better customer experience.

Let’s look at a few other examples of big data-driven innovations:

Personalized User Experience with Customer Profiling

By combining incoming data to build customer profiles, you can better understand what motivates a customer to purchase. Compiling multiple touch points from different channels in real-time allows you to create a holistic profile. After purchase, you can build a more robust profile with basket analysis and leverage data mining to understand more about who your shoppers are, including their age, income range, buying habits, and preferences. Customer profiles allow e-commerce companies to customize the shopping experience.

Dynamic Pricing

As we mentioned, the e-commerce market is hyper-competitive. Reduced customer loyalty means that shoppers will hunt for the best price. e-Commerce companies need dynamic pricing to remain competitive. Data needs to be gathered from multiple sources including competitor pricing, sales, regional preferences, and customer actions.

Customer Service

Exceeding expectations in customer service will build loyalty and generates word of mouth. With robust customer profiles that connect data sources, when a customer calls to complain, the customer services agent can see that what the customer has already said on contact forms and social media. This data equips the agent with more information to satisfy the guest.

Predictive Analytics

Know what is going to happen before it does. Big data powers machine learning algorithms that predict events. This feeds into supply chain visibility by understanding sales patterns to ensure product availability and avoid out-of-stock items.

Big data can also be a Big problem

All of this data and the need to remain competitive is driving technology innovation in e-commerce. While it is the data that enables innovation, the data also hampers progress. Leveraging big data is a big challenge. E-commerce companies are drowning in data. The amount of data, the variety of data, and the speed at which it is piling up are the biggest hurdles for e-commerce companies to overcome when looking to leverage their data.

Amount: Insights and product teams have access to so much data that they are overwhelmed.

Variety: Data comes from multiple channels and in different formats. It’s impossible for machines to make sense of this abundance of unstructured data.

Velocity: The heap of unstructured data is growing rapidly every single day.

e-Commerce companies can’t be data-driven without first understanding what the data is saying. For technology innovations to be leveraged, this abundance of data must first be structured. Machines aren’t currently capable of doing this; humans must intervene. From the product side, data labeling takes up the majority of a data scientist’s time. This time would be better spent focusing on core projects that will provide e-commerce companies with the competitive advantages needed today.

Human-in-the-loop computing unlocks the value hidden within data at scale

Product teams are hindered by the amount of data that requires labeling. Recent innovations such as increased bandwidth and cloud computing have created a newly digitized economy across the globe providing opportunity in places where they previously did not exist. At iMerit, we constantly encounter unexpected use cases where customers are exploring their data and call upon us to unlock parts of the trove to improve the customer experience.

By handing off data labeling requirements to data specialists, product teams can focus on the innovations that will provide the competitive edge required today. Our scalable, on-demand workforce provides e-commerce companies with data services that promote:

  • Increased search relevance providing a better customer experience
  • Conversion rate optimization through up-selling and dynamic pricing opportunities
  • Enhanced customer service with converged data

To remain competitive, e-commerce companies need to be focused on what their customers want. On-demand workforces allow you to outsource the data work while remaining focused on what’s next.

Do you have data needs? Launch a custom workforce to handle your data work today.

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Blog, train ai

Today is the day! The iMerit team is very excited to be attending CrowdFlower’s Train AI conference again this year as a premiere NDA partner and Silver Sponsor. iMerit partners with CrowdFlower to offer you a secure option for your sensitive and critical data projects through iMerit’s curated team of over 800 in-house data experts. There are so many great Artificial Intelligence conferences to attend every year. While many conferences […]

Today is the day! The iMerit team is very excited to be attending CrowdFlower’s Train AI conference again this year as a premiere NDA partner and Silver Sponsor. iMerit partners with CrowdFlower to offer you a secure option for your sensitive and critical data projects through iMerit’s curated team of over 800 in-house data experts.

There are so many great Artificial Intelligence conferences to attend every year. While many conferences will focus on the algorithms, Train AI takes a different approach focusing on the other 90% of the work, the data preparation, training data, and feature deployment.

What to look forward to at Train AI

There are so many great speakers at this year’s event. Here are a few that we are really looking forward to hearing from.

Creating Software with Machine Learning: Challenges and Promise

Peter Norvig, the Director of Research at Google will be how the traditional programmer’s role is being replaced by a trainer. The trainer’s role is to show the computer examples until it learns to complete the task. Peter will explore the exciting opportunities that this shift presents.

Computer Vision: helping machines see and understand the world

Lukas Biewald, the founder of CrowdFlower, will be going over all of the changes that CrowdFlower has experienced over the past year from deep learning and computer vision applications. He’ll be talking about use case from a variety of industries including healthcare, self-driving cars, drones, and animal conservation using tools that support their customer’s labeling and human-in-the-loop workflows.

Amplifying machine learning with human intelligence

Toby Segaran, the Director of Engineering at Reddit will explore ways that human intelligence can be used during the process of optimization, topic modeling, and entity resolution to improve algorithmic results as they’re happening. This stems from the fact that the most common approaches to using machine learning often uses humans to initially label a dataset for training and quality checks.

Reaching unparalleled quality with human-in-the-loop

Maran Nelson is the Co-Founder & CEO at Clara Labs. She will be discussing how human-in-the-loop processes are increasingly the best pathway to reach a high-achieving system. Maran will discuss the implementation of this hybrid structure that takes what works best in AI and humans and creates outperforming products.

Tackling The Limits of Deep Learning

Rochard Socher, the Chief Scientist at Salesforce, will be discussing the problems and limitations of Deep Learning. In this talk, Richard will be going over solutions to some of these including: How to predict previously unseen words at test time, how to have a single input and output encoding for words, how to grow a single model for many tasks, and how to use a single end-to-end trainable architecture for question answering.

Are you Attending Train AI?

If you’re at the event today, be sure to stop by the iMerit booth, say hi to our awesome team, and learn about how iMerit partners with CrowdFlower to offer NDA Channel services. If you have data labeling projects that are sensitive or require a high degree of security, come chat with us!

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