How Big Data Analytics, AI and Machine Learning is Being Leveraged Across FinTech
I am sure that you have heard of one of the only profitable FinTech unicorns in the world: Klarna. A customer making an online purchase enters only their email address and zip code on an e-commerce merchant site to buy an item. Klarna pays that merchant immediately and then collects the amount due from the consumer within 14 days. Imagine the amount of work the engines in the background are doing. Today, I will be talking about that area/segment a bit. The use of analytics in its many forms – big data, data science and many more – is not a new concept in FinTech. The growth in data or data explosion is a function of multiple technological advancements. Adoption of cloud, mobile technologies, apps, wearable devices, intelligent/smart networks, and Internet penetration/usage are some of the major factors for growth in overall data. To put this into perspective, IDC estimated that the digital universe is doubling its size yearly and would reach 44 ZB in 2020 from 4.4 ZB of data generated in 2013. It also forecasted that the big data technology and services market will grow at a 26.4% compound annual growth rate to $41.5 billion through 2018, or about six times the growth rate of the overall information technology market. The ability to draw insights and the ability to optimally monetize available data would place companies in a unique position challenging established rules and processes. Low-cost storage technology, smartphone penetration and cloud are underlying forces which propel the requirement of big data and analytics.
Analytics and Big Data in the US Financial Services Industry (Deep-dive Insight)
The use of analytics – otherwise called big data, machine learning, data science and many more – in FinTech is not a new concept. The growth in data or data explosion is a function of multiple technological advancements. Adoption of cloud, mobile technologies and apps, wearable devices, intelligent/smart networks and systems, Internet penetration and usage, are some of the major factors for growth in overall data. To put this into perspective, IDC estimated that the digital universe is doubling its size yearly and would reach 44 ZB in 2020 from 4.4 ZB of data generated in 2013. It also forecasted that the big data technology and services market will grow at a 26.4% compound annual growth rate to $41.5 billion through 2018, or about six times the growth rate of the overall information technology market. The ability to draw insights and the ability to optimally monetize available data would place companies in a unique position, challenging established rules and processes. Low-cost storage technology, smartphone and app usage, and cloud are underlying forces which propel the requirement of big data and analytics.
Big Data Scoring
Big Data Scoring is a credit scoring company that develops generic and tailored credit score models to its customers based on big data and social networks.
Brand Big Data (BBD)
Brand Big Data (BBD) provides a Big Data solutions (HIGGS Kunlun and HIGGS Galaxy) for the finance industry. HIGGS Kunlun provides a platform to build applications that allows business users to draw meaningful insights into financial unstructured data sourced from different sources to analyze on credit risk whereas HIGGS Galaxy is suitable for large-scale quantitative investigation.
Racing Into Machine Learning: Data Readiness & the Developing World
Machine Learning (ML) technology can help us draw important insights from data, but it is imperative to recognize a model is not an end in and of itself. Based on BFA’s experiences engaging with early-stage partners in emerging markets, such as Catalyst Fund investees, we have seen the consequences of rushing into machine learning without a clear understanding of the underlying data.
Features Analytics Launches eyeDES 4.0 Machine Learning Risk Management Platform for Better Accuracy & More Stability
Belgian company Features Analytics will be present at the Money 20/20 startup exhibit hall this October in Las Vegas. Co-founder and CEO Cristina Soviany will present the new Features Analytics’ eyeDES 4.0 machine learning solution. The eyeDES platform for scoring risk and fraud is equipped with powerful automatic model retrain functionality, adding more resilience and user-friendliness and is aimed at large financial institutions and large brand merchants.
Worldwide Revenues for Big Data and Business Analytics Will Surpass $200 Billion in 2020
Big data is an immense part of any innovative business and a fuel for sophisticated decision-making algorithms in the financial services industry and beyond. The importance of the stream of records on customer behavior (whether financial or not) is difficult to overestimate as it provides companies an opportunity to make accurate business choices and stay relevant in the market.
7 Payments & Commerce Startups using Big Data Analytics
The payments business is increasingly being driven by information. Strategies and execution through information insights can improve profitability, optimize revenue, and cut costs.. Big Data Analytics can introduce some major applications that result in reduction in payments fraud occurrences and in building a body of knowledge from customer data points in order to structure value-added services and create opportunities for cross selling. Here are some notable startups in the payments industry optimizing their solutions through big data analytics:
MasterCard Acquires Applied Predictive Technologies for Big Data Analytics
Cloud-based analytics and Big Data company, Applied Predictive Technologies (APT), has been acquired by MasterCard for $600 million.
FinTech Week in Review: Mimicry, Machine Learning, and the Business of FinTech
This week, I’m inspired by Ben Thompson and James Allworth’s podcast, Exponent. A pitfall of our technology-obsessed business culture is that we forget technology itself is never enough. Technology must be applied in the right situation, in a sustainable business model, and with reasonable checks and balances. Financial services have learned a few things over the decades. We shouldn’t throw out every customer insight and business process we’ve developed. The trick is, what should we keep and what is obsolete?
Insurers, Big Data and Changing Consumer Behavior
It’s not about having data, but what to do with it, right? Recently, someone suggested a new life insurance product to test out what would be a better fit for me than the traditional product I already had. The catch was that this new program would utilize the big data on my smartphone, connected devices and actually tracked my lifestyle, rather than relying on standards and doctors reports. True to my geek nature, I leaped at the chance to test this out.
Analytics, ML and Data Science Help FinTech Offer Better Services
The use of big data, machine learning, data science in FinTech space is not a new concept (at least from the sound of it). The growth in data or data explosion is a function of multiple technological advancements. Adoption of cloud, mobile technologies and apps, wearable devices, intelligent/smart networks and systems, Internet penetration and usage are some of the major factors for growth in the overall data. We wanted to understand how FinTech players are making use of it (or not). Some of the areas in financial services that are applying analytics, ML and big data are listed below:
The Third Wave: Analytics (a.k.a. “Big Data”) Will Transform the US Payments Infrastructure
The LTP team had previously estimated the creation of 40,000 new jobs in payments and commerce. Not surprisingly, many of these opportunities are in technology and data sciences. As the large FIs continue to expand and improve their tech capabilities and the tech & mobile players begin building at scale to support payments & commerce, product and development skills will be in increasing demand.
The Future of Data Management and Analytics Is in Data-As-A-Service (DaaS)
The concept of DaaS is not new to the market, but an increasingly important one, given the accelerating growth and complexity of data accumulated by organizations across industries. As experts explain, the big picture idea behind the DaaS model is all about offloading the risks and burdens of data management to a third-party cloud-based provider. DaaS is a way of accessing business-critical data where it resides.
Applications of Machine Learning in FinTech
Machine learning is a type of artificial intelligence that provides computers with the ability to learn without being explicitly programmed. The science behind machine learning is interesting and application-oriented. Many startups have disrupted the FinTech ecosystem with machine learning as their key technology.
Founder of http://Ravelin.com - Go micro-services platform using machine learning, graph networks and real time big data analysis to detect online fraud.
15 Players that Use Machine Learning in FinTech Space
Machine learning is a type of artificial intelligence that provides computers with the ability to learn without being explicitly programmed. Machine learning focuses on the development of computer programs that can teach themselves to grow and change when exposed to new data. Emerging FinTech companies are using machine learning to adapt the changes in real time.
3 Components Necessary for Success in Any Company’s Data and Analytics Journey
No one can dispute that data has significant value for organizations. We see it every day in how they use data to successfully deliver better customer experiences – whether that means more personalization or better products and services based on collected and analyzed customer behavior.
The Emergence of the Internet-of-Things and Big Data – August of Money Podcast
In the fifth installment of our continuing podcast series, “August of Money – The Quest for Cashless Society”, host Aditya Khurjekar has a futuristic discussion with special guest Atsushi Taira of Softbank and author Mehul Desai, looking at the emergence of the Internet-of-Things, (Big) Data as the new currency, and its impact on everything from electricity generation, water consumption, and the inevitable march towards a “Cashless” society. Take a listen!
Founder @germin8 * Artificial Intelligence * Natural Language Processing * Machine Learning * Social Media Analytics * Entrepreneurship
Big Data: Friend or Foe?
Big data has become an immense part of any sort of sophisticated decision-making tool for financial institutions. The importance of consolidated structured records on customer financial (and not only) behavior is difficult to overestimate as it provides companies an opportunity to make accurate business choices and stay relevant in the market.
Innervative Learning is in the game of revolutionizing the delivery of financial content to customers. We focus on financial education through Gamification – an effective avenue to ENTERTAIN, EDUCATE and ENGAGE target audience through the power of play.
Big Squid provides a machine learning-based, self-serve predictive analytics platform to executive decision makers. It provides data solutions by utilizing business intelligence, data sciences, digital marketing, and predictive analytics. The company offers a Domo platform and business intelligence consulting solutions for retail, insurance, real estate, media, professional services, healthcare, and manufacturing sectors.
Splice Machine enables its clients to build big data apps with all the benefits of NoSQL databases while leveraging the strengths of SQL. It optimizes complex queries without rewriting existing SQL-based apps and BI tool integration.
Lending Club Loan Data Analytics Part 3, based on 4 Mn+ Data Points
Continuing in the series after the hugely popular Parts 1 & 2 of the exclusive in-depth analysis of the loan data of Lending Club, we present some additional insights based on the same data. In the previous two articles, we had covered certain aspects of how loans work in Lending Club. In this article, we bring insights around additional factors attributed to loan applications and how loans and interest are paid off.