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What you need to know: The Future of FinTech, RegTech and Wealth Management in the Digital Space

The tipping point is here. High tech business intelligence tools with built-in machine learning algorithms and big data inputs were once reserved for the Fortune 500. Now, the FinTech fad has shifted from early stage adopter to mainstream money manager and former technophobes are starting to digitize their businesses from end to end. New low cost, user-friendly self-service tools that can produce rapid-fire insights and on-demand customer service are finally within reach and can provide family wealth managers the brain power they need without the additional headache. Synaptik, True Interaction’s “Plug, Play, Predict” machine platform is already serving companies in the space, providing value more quickly than industry norms.

Reuters white paper on the digitization of wealth management identified three drivers behind the mainstream movement towards FinTech:

– New tools for investment research, risk management, trade processing, compliance, and reporting
– New business models offering better, faster, cheaper variants of existing services in investment management and brokerage
– New marketplaces, new managers, and new financial products that are changing the way capital and risk are allocated

In this blog post, we’ll explore disruptive technologies traditional firms with limited IT expertise can use to beat the market, improve existing services and stay on top of an increasingly complex regulatory environment. By leveraging cloud, open source, big data, Artificial Intelligence, API and Chatbots, companies can create robust digital ecosystems that will win younger clients and increase profits across the board. Companies that continue to resist digital transformation run the risk of becoming less competitive while those that embrace the opportunity will benefit from supplementing talented human capital with technological know-how.

Courtesy of PWC

Beat the Market

Big name hedge funds and investment firms deploy AI to comb through the internet for new investment opportunities. The elusive “super-algo” can swallow huge amounts of information from news reports, databanks and social media platforms and quickly optimize portfolios to profit from microscopic ripples and seismic shifts in the market. While private family wealth managers have relied on traditional methods and experience to pinpoint good investment opportunities, machine learning can provide the edge they need to compete in a volatile world. Now, building data ecosystems that provide real-time information and time series data on company performance and consumer trends no longer requires a Ph.D. in data analytics or computer science.

When considering investment management software, companies should look for some key features including scenario simulation, modeling, portfolio rebalancing, performance metrics, yield curve analysis and risk analytics. Your software should also be flexible, adaptable and able to ingest structured and unstructured data. The costs of professional investment programs range from $1300 to $8000 but as the market matures costs are likely to go down.

Money Management on Demand

Wealth management firms have relied on traditional relationship-driven business models for decades. But the personal touch that keeps more senior clients happy may repel the next generation. To attract younger clientele, companies need to invest in on-demand, low-touch digital customer service models that provide better transparency and more autonomy to their clients. Creating a flexible digital strategy that allows different client segments to engage with their portfolio independently and with their advisor as little or as often as they want is key to success. EY’s report “Advice goes virtual” looks at the range of innovative wealth management models that are now available and highlights firms that have struck the perfect balance between automation and human capital. Companies like Personal Capital, Future Advisor and LearnVest provide digital platforms with phone-based financial advisor services to meet the needs of busy millennials and satisfy the clients that prefer a dedicated human that knows the future they want to make for themselves. EY’s chart on innovations in wealth management sums up the range of digital opportunities that clients are gravitating towards.


Courtesy of EY

Automated Compliance

Since the financial crisis, the cost of compliance has risen steeply. Tech Crunch reports that “the global cost of compliance is an estimated $100 billion per year. For many financial firms, compliance is 20% of their operational budget.” Innovations in RegTech, an offspring of FinTech, can automate certain components of the compliance process and have the potential to dramatically reduce the cost of doing business. The Institute for International Finance (IIF) defines “RegTech” as “the use of new technologies to solve regulatory and compliance requirements more effectively and efficiently.”

Since 2008, the increasing speed of regulatory change has kept wealth management firms in a state of paralysis. Companies are constantly playing catch up and readjusting procedures to meet new requirements. In the not so distant future, integrated RegTech solutions will connect directly with regulatory systems and automatically update formulae, allowing wealth management firms to refocus their resources on revenue generating activities.

Instead of producing lengthy paper reports for regulators, new RegTech solutions can generate and communicate required reports automatically. Instead of scouring hundreds of documents and spreadsheets on a quarterly basis, RegTech solutions will alert compliance managers to risks in real-time so they can be eliminated immediately. The possibilities are endless and the cumbersome and costly task of navigating the increasingly complex regulatory environment will continue to generate more innovations in this field. While RegTech is still in its infancy, small family wealth management firms should start investigating this growing subsector and use this disruptive technology to their advantage.

Traditional wealth management firms that continue to resist the digital revolution will begin to look antiquated, even to their most senior clientele. True Interaction specializes in building and executing digital transformation strategies for companies that don’t have IT expertise. Synaptik, True Interaction’s CMS for data, is already providing firms in the FinTech, RegTech and AdTech spaces with easy-to-use data management, visualization, and deep learning insights. Our experts are providing free consultations to help them assess their needs and start planning their digital future. Schedule your custom consultation here.

By Nina Robbins

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Can Artificial Intelligence Catalyze Creativity?

In the 2017 “cerebral” Olympic games, artificial intelligence defeated the human brain in several key categories. Google’s AlphaGo beat the best player of Go, humankind’s most complicated strategy game; algorithms taught themselves how to predict heart attacks better than the AHA (American Heart Association); and Libratus, an AI built by Carnegie Mellon University, beat four top poker players at no-limit Texas Hold ‘Em. Many technologists agree that computers will eventually outperform humans on step-by-step tasks, but when it comes to creativity and innovation, humans will always be a part of the equation.

Inspiration, from the Latin inspiratus, literally means “breathed into.” It implies a divine gift – the aha moment, the lightning bolt, the secret sauce that can’t be replicated. Around the globe, large organizations are attempting to reculture their companies to foster innovation and flexibility, two core competencies needed to survive the rapid-fire rate of change. Tom Agan’s HBR article titled “The Secret to Lean Innovation” identified learning as the key ingredient, while Lisa Levey believes that seeing failure as a part of success is key.

At the same time, although innovation is a human creation, machines do play a role in that process. Business leaders are using AI and advanced business intelligence tools to make operations more efficient and generate higher ROI, but are they designing their digital ecosystems to nurture a culture of innovation? If the medium is the message, then they should be.

“If you want to unlock opportunities before your competitors, challenging the status quo needs to be the norm, not the outlier. It will be a long time if ever before AI replaces human creativity, but business intelligence tools can support discovery, collaboration and execution of new ideas.” – Joe Sticca, COO at Synaptik

So, how can technology augment your innovation ecosystem?

Stop

New business intelligence tools can help you manage innovation, from sourcing ideas to generating momentum and tracking return on investment. For instance, to prevent corporate tunnel vision, you can embed online notifications that superimpose disruptive questions on a person’s screen. With this simple tool, managers can help employees step outside the daily grind to reflect on the larger questions and how they impact today’s deliverable.

Collaborate

The market is flooded with collaboration tools that encourage employees to leverage each other’s strengths to produce higher quality deliverables. The most successful collaboration tools are those that seamlessly fit into current workflows and prioritize interoperability. To maximize innovation capacity, companies can use collaboration platforms to bring more diversity to the table by inviting external voices including clients, academics and contractors into the process.

Listen

Social listening tools and sentiment analysis can provide deep insights into the target customer’s needs, desires and emotional states. When inspiration strikes, innovative companies are able to prototype ideas quickly and share those ideas with the digital universe to understand what sticks and what stinks. By streamlining A/B testing and failing fast and often, agile companies can reduce risk and regularly test their ideas in the marketplace.

While computers may never birth the aha moments that drive innovation, advanced business intelligence tools and AI applications can capture sparks of inspiration and lubricate the creative process. Forward-thinking executives are trying to understand how AI and advanced business intelligence tools can improve customer service, generate higher ROI, and lower production costs. Companies like Cogito are using AI to provide real-time behavioral guidance to help customer service professionals improve the quality of their interactions while Alexa is using NLP to snag the full-time executive assistant job in households all over the world.

Creativity is the final frontier for artificial intelligence. But rather than AI competing against our innovative powers, business intelligence tools like Synaptik can bolster innovation performance today. The Synaptik difference is an easy user interface that makes complex data management, analytics and machine learning capabilities accessible to traditional business users. We offer customized packages that are tailored to your needs and promise to spur new ideas and deep insights.

By Nina Robbins

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Insights

New York Civic Tech Innovation Challenge – Finalist

The Neighborhood Health Project is a 360° urban tech solution that takes the pulse of struggling commercial corridors and helps local businesses keep pace with competition.

New York City’s prized brick-and-mortar businesses are struggling. With the rise of e-commerce, sky high rents and growing operational costs, the small businesses that give New York City Streets their distinctive character face mass extinction.

This year’s NYC Department of Small Business Services Neighborhood Challenge 5.0 paired nonprofit community organizations and tech companies to create and implement tools that address specific commercial district issues. On June 15th, community-based organizations from across the city from the Myrtle Avenue Brooklyn Partnership to the Staten Island Economic Development Corporation, presented tech solutions to promote local business and get a deeper understanding of the economic landscape.

The Wall Street Journal reports that “the Neighborhood Challenge Grant Competition is a bit like the Google Lunar XPrize. Except rather than top engineers competing to put robots on the moon, it has tiny neighborhood associations inventing new methods to improve business, from delivery service to generating foot traffic.”

Synaptik, the Manhattan Chamber of Commerce and the Chinatown BID were thrilled to have their Neighborhood Health Project chosen as a finalist in this year’s competition.

The Neighborhood Health Projects aims to preserve the personality of our commercial corridors and help our small businesses and community at large adapt to the demands of the 21st century economy. By optimizing data collection, simplifying business engagement and integrating predictive analytics, we can get a better understanding of the causes and effects of commercial vacancies, the impacts of past policies and events and create an open dialogue between businesses, communities and government agencies.

“With Synaptik, we can provide small businesses user-friendly tools and data insights that were previously reserved for industry heavy weights with in-house data scientists and large resource pools” said Liam Wright, CEO of Synaptik.

The Neighborhood Health Project Team was honored to have had the opportunity to share the stage with such innovative project teams. “It is great to see civic organizations take an innovative role in data intelligence to serve community constituents and local businesses. We came far in the process and hope to find alternative ways to bring this solution to New York City neighborhoods ” said Joe Sticca, Chief Operating Officer of Synaptik.

By Nina Robbins

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Data, Automation & AI Featured

AI and the Classroom: Machine Learning in Education

Situation

For years schooling has been typified by its aspect of the physical grind on the part of both students and their teachers: teachers cull and prepare educational materials, manually grade students’ homework, and provide feedback to the students (and the students’ parents) on their learning progress. They may be burdened with an unmanageable number of students, or a wide gulf of varying student learning levels and capabilities in one classroom. Students, on the other hand, have generally been pushed through a “one-size-fits-all” gauntlet of learning, not personalized to their abilities, needs, or learning context. I’m always reminded by this quote by world-renowned education and creativity expert Sir Ken Robinson:

“Why is there this assumption that we should educate children simply according to how old they are? It’s almost as if the most important thing that children have in common is their date of manufacture.”

But as the contemporary classroom has become more and more digitized, we’ve seen recent advances in AI and machine learning that are closing in on being able to finally address historical “hand-wrought” challenges – by not only collecting and analyzing data that students generate (such as e-learning log files) when they interact with digital learning systems, but by pulling in large swaths of data from other areas including demographic data of students, educator demographic and performance data, admissions and registration info, human resources information, and so forth.

Quick Review: What is Machine Learning?

Machine learning is a method of data analysis that automates analytical model building. Using algorithms that iteratively learn from data, machine learning allows computers to find hidden insights without being explicitly programmed where to look Machine learning works especially well prediction and estimation when the following are true:

-The inputs are well understood. (You have a pretty good idea of what is important but not how to combine them.)
-The output is well understood. (You know what you are trying to model.)
-Experience is available. (You have plenty of examples to train the data.)

The crucible of machine learning consists of capturing and maintaining a rich set of data, and bringing about the serendipitous state of knowledge discovery: the process of parsing through the deluge of Big Data, identifying meaningful patterns within it, and transforming it into a structured knowledge base for future use. As long as the data flows, its application is endless, and we already see it everywhere, from Facebook algorithms to self-driving cars. Today, let’s examine machine learning and its implementation in the field of Education.

Application of Machine Learning in Education

Prediction

A few years ago, Sotiris Kotsiantis, mathematics professor at the University of Patras, Greece presented a novel case study describing the emerging field of educational data mining, where he explored using students’ key demographic characteristic data and grading data in a small number of written assignments as the data set for a machine learning regression method that can be used to predict a student’s future performance.

In a similar vein, GovHack, Australia’s largest open government and open data hackathon included several projects in the education space, including a project that aims to develop a prediction model that can be used by educators, schools, and policy makers to predict the risk of a student to drop out of school.

Springboarding from these two examples, IBM’s Chalapathy Neti recently shared IBM’s vision of Smart Classrooms: cloud-based learning systems that can help teachers identify students who are most at risk of dropping out, why they are struggling, as well as provide insight into the interventions needed to overcome their learning challenges:

The system could also couple a student’s goals and interests with data on their learning styles so that teachers can determine what type of content to give the student, and the best way to present it. Imagine an eighth grader who dreams of working in finance but struggles with quadratic and linear equations. The teacher would use this cognitive system to find out the students learning style and develop a plan that addresses their knowledge gaps.

Process efficiency: Scheduling, grading, organization

Elsewhere, several Machine Learning for Education ICML (international machine learning conference) workshops have explored novel machine learning applications designed to benefit the education community, such as:

-Learning analytics that build statistical models of student knowledge to provide computerized and personalized feedback on learning the students’ progress and their instructors
-Content analytics that organize and optimize content items like assessments, textbook sections, lecture videos, etc.
-Scheduling algorithms that search for an optimal and adapted teaching policy that helps students learn more efficiently
-Grading systems that assess and score student responses to assessments and computer assignments at large scale, either automatically or via peer grading
-Cognitive psychology, where data mining is becoming a powerful tool to validate the theories developed in cognitive science and facilitate the development of new theories to improve the learning process and knowledge retention
-Active learning and experimental design, which adaptively select assessments and other learning resources for each student individually to enhance learning efficiency

Existing Platforms

Recently, digital education venture capitalist Tom Vander Ark shared 8 different areas where leading-edge platforms are already leveraging machine learning in education:

1. Content analytics that organize and optimize content modules:
a. Gooru , IBM Watson Content Analytics

2. Learning analytics that track student knowledge and recommend next steps:
a. Adaptive learning systems: DreamBox, ALEKS, Reasoning Mind, Knewton
b. Game-based learning: ST Math, Mangahigh

3. Dynamic scheduling matches students that need help with teachers that have time:
a. NewClassrooms uses learning analytics to schedule personalized math learning experiences.

Grading systems that assess and score student responses to assessments and computer assignments at large scale, either automatically or via peer grading:
a. Pearson’s WriteToLearn and Turnitin’s Lightside can score essays and detect plagiarism.

5. Process intelligence tools analyze large amounts of structured and unstructured data, visualize workflows and identifying new opportunities:
a. BrightBytes Clarity reviews research and best practices, creates evidence-based frameworks, and provides a strength gap analysis.
b. Enterprise Resource Planning (ERP) systems like Jenzabar and IBM SPSS helps HigherEd institutions predict enrollment, improve financial aid, boost retention, and enhancing campus security.

6. Matching teachers and schools:
a. MyEdMatch and TeacherMatch are eHarmony for schools.

7. Predictive analytics and data mining to learn from expertise to:
a. Map patterns of expert teachers
b. Improve learning, retention, and application.

8. Lots of back office stuff:
a. EDULOG does school bus scheduling
b. Evolution , DietMaster.

Reflection

As the modern classroom becomes more and more digitized, we are able to gather myriad sets of data. The trick is, of course, being able to purpose it. The prize at heart of machine learning is knowledge discovery, the process of parsing through the deluge of Big Data, identifying meaningful patterns within it, and transforming it into a structured knowledge base for future use. In this article, we’ve seen examples utilizing machine learning in the education sector for prediction, scheduling, grading, and organization. We’ve also listed existing education-related platforms that use a machine learning component.

What does it mean to me?

Big Data have swept into every industry and business function and are now an important factor in production, alongside labor and capital. In a decision making system, the bigger the data, the higher the likelihood is of making good decisions. The time is now for organizations, in education or otherwise, to research how a cost-efficient machine learning component can transform your operational output. For more information, Check out this detailed guide by Jesse Miller on the amazing benefits of technology in the classroom and suggestions on ways to incorporate technology in the classroom.

“Parents are continually exposed to new technology via their children. Whether it be iPad App usage tricks, to the advent of robotics competitions, and perhaps now “new ways of thinking” as a result of interaction with Machine Learning based educational environments. Siloed educational content may give way to a topology of learning experiences.” O. Liam Wright – CEO, True Interaction

True Interaction produces custom full-stack end-to-end technology solutions across web, desktop and mobile, integrating multiple data sources to create a customized data solution. True Interaction can determine the most optimal means to achieve operational perfection, devising and implementing the right tech stack to fit the specific school and or district need. True Interaction pulls together disparate data sources, fuses together disconnected silos, and does exactly what it takes for school data systems to operate with high levels of efficiency and efficacy, ultimately leading to improved student achievement outcomes.