Contributed by Logicworks
Written by Steve Zeller, VP of Product Marketing, Logicworks

“Machine learning is so tantalizing for most everyday developers and scientists. Still, there are a lot of constraints for builders….How do we turn machine learning from a capability of the few, into something that many more people can take advantage of?” – Andy Jassy, Keynote from AWS re:Invent 2017

The FinTech industry has been hyped about the potential of machine learning technology for years. Despite all the noise, it’s still very early for most companies. Expert machine learning practitioners are rare, and even if they manage to find one, it usually takes more than a year to launch a machine learning app into production.

But all that’s set to change in 2018. Most important, SaaS-based machine learning platforms are maturing and ready for use by FinTech companies. Equally exciting are the tools made available by Amazon Web Services (AWS) — the platform most FinTech companies are already running on — to make the process of building their own machine learning algorithms much easier.

SaaS Machine Learning Platforms for FinTech
Creating a machine learning model isn’t easy. Start by getting data in one place, and then choose an algorithm, train the model, tune the model, deploy the model, and fine-tune it over time. Given the pace of change in the industry, algorithms need to be tuned constantly. But data analysis power is not enough. The tougher job is to understand how to communicate the insights of machine learning to consumers.

Given all these challenges, finance companies usually begin by searching for a SaaS-based machine learning platform that solves an existing challenge, rather than building their own tool. For finance companies that ingest large amounts of financial data, machine learning means using data from thousands of consumers to pinpoint investment opportunities, uncover fraud, or underwrite a loan.

Here are some of the popular SaaS machine learning apps and APIs for finance:

User Logins and Facial Recognition
User login processes are changing and having usernames and passwords to access a bank account might not be around forever. Technology for facial recognition and biometrics is finally reaching the mainstream; Facebook facial recognition finds photos people are untagged in; facial recognition cameras have been installed in apartment complexes in China to provide keyless entry; facial scanning pilot programs are currently in use in six American airports. In late 2017, Amazon released DeepLens, “the world’s first deep learning enabled video camera”, which will likely spur further innovation in facial recognition.

  • Kairos – a “human analytics” platform for face detection, identification, emotion detection, and more. It’s already used by companies such as Carnival, Pepsico, IPG, and more.
  • IBM Watson Visual Recognition API– an API that allows people to tag and classify visual content, including faces.

Portfolio Management
Companies such as Betterment, Mint and others have proven that millennial customers don’t need to speak with a human advisor to feel comfortable investing. Instead, they trust algorithms that change their investments according to market changes. These complex, machine-learning-led services are taking significant market share from more traditional advisory channels.

  • ai– A platform used by private wealth managers and institutions to provide clients with a digital experience to track investments, plus automated recommendations. It also provides analytics to wealth managers across their client base.
  • Clinc – A conversational AI platform for personal banking. Clinc can provide wealth managers’ clients with personalized insight into spending patterns, notify customers of unusual transactions, and recommend new financial products.

Fraud Detection
According to the Association of Certified Fraud Examiners, the money lost by businesses to fraud is over $3.5 trillion every year. Machine learning-based platforms help warn companies of potential fraudsters or phishing attacks in real time.

  • Kount– A platform that allows fraud identification in real time. Kount AI Services combines their core platform with custom machine learning rules developed by their data science team.
  • IBM Trusteer– IBM’s Pinpoint Detect is a cloud-based platform that correlates a wide range of fraud indicators to detect phishing attacks, malware, and advanced evasion methods.

Still Want to Build Your Own? Consider Machine Learning on AWS
Finance companies that want to build proprietary machine learning algorithms will not be satisfied with a one-size-fits-all SaaS tool. If you want to build your own machine learning app, AWS can significantly reduce the amount of time it takes to train, tune, and deploy your model.

AWS has always been at the forefront of machine learning; think of Amazon’s recommendation engine that displays products that customers like you have purchased, or Amazon Echo, the popular voice-controlled smart home hub. They’ve released a series of machine learning tools over the past 3 years for their AWS customers, including the technology behind Echo’s Alexa.

At re:Invent 2017, Amazon released a service that packages many of their previously-announced machine learning capabilities into an easy-to-use, fully-managed service: AWS SageMaker.

Source: AWS

SageMaker is designed to empower any developer to use machine learning, making it easy to build and train models and deploy them into production. It automates all the time-consuming training techniques and has built-in machine learning algorithms, so you can get up and running quickly. Essentially, it’s one-click machine learning for developers; you provide the data set, and it’ll give you some interesting outputs. This is a big deal for smaller companies without a fleet of data scientists who want to build machine learning applications. Granted, developers still have to understand what they’re doing and apply that model in a useful way to your customers.

Summary
The hype cycle for machine learning is far from over, so expect a deluge of product releases and marketing emails this year. But as with most technology shifts, the greatest barrier to adoption is a cultural mindset – not a lack of tools. Business leaders need to prioritize IT innovation and a culture of experimentation for their companies to adopt machine learning.

 

About Logicworks
Logicworks, the leader in compliant cloud solutions, provides end-to-end professional services, cloud management, and cloud security to clients in the finance, healthcare, and SaaS industries. Visit us at www.logicworks.com.