Under the latest International Data Corporation (IDC) forecast, the market for big data companies is expected to grow to $48.6 billion in 2019 having a compound annual growth rate of 23.1%.
The amount of digital information created globally in 2005 was less than 0.2 of zettabyte. In 2020, the world is expected to produce totally more than 34 zettabytes of digital information.
It’s obviously that the growing amount of data will push big data market and consequently cause a growth of big data companies.
Meanwhile, the latest trends in fintech market show that the big data solutions will allow fintech companies to stay above the curve in financial world. Big data companies could help to create new profit sources and better customer experiences.
A lot of fintech companies try to use big data independently gathering and analyzing a wide range of data points including unstructured social media data such as tweets, blogs, tags, status updates, pins, etc. Putting all this data together could help businesses to create a bigger, sharper picture of its needs and abilities.
However, big data analysis is not an easy thing. Usually, fintech companies face standard set of problems while managing big data:
- Big data is not big insights – data by itself doesn’t provide a decision maker with the information that is need to make a proper decision. It’s a point where a fintech company needs a set of analytic tools and, moreover, a good data scientist (or a couple of them) who knows how to work well with large volumes of data and big data sets, and who has high skills for applying predictive analytics to big data. Each company needs the right people to help make sense of the data that are gathered. This data should be correctly transformed into a form that allows to make helpful conclusions and properly apply them;
- How to Storage it? – even small and medium amounts of data can be difficult to manage in terms of storage. The more data a company has the more complex issues it should solve. To buy or not to buy special hardware for storage (HDD vs SSD)? To store or not to store in a cloud? These and many other issues should be solved before a company make a decision to manage big data independently;
- Keep the privacy – this issue actually has two sides. On the one hand, a company should keep gathered data secured, on the other hand, companies actions taken in order of big data analytics may breach someone’s privacy and lead to respective legal consequences. Therefore, companies that manage big data should have strong cybersecurity and legal support. Recently we’ve published the list of the best cybersecurity companies for fintech companies.
All the factors mentioned above persuade a lot of fintech companies to use the services of the big data companies that provide businesses with turnkey solutions related to big data usage.
Below you will find some of the best big data companies that can help fintech businesses to use big data in the most convenient and effective way.
Personetics is one of Tel Aviv-based big data companies that offers a predictive interaction solution designed specifically for the financial services industry. It tries to predict a customer’s intents leveraging a real-time analytics engine. The company helps all types of financial institutions to deliver more personalized customer experience across all service channels, especially online, mobile, and tablet.
Personetics leverages what a company knows about its customers (including historical behavior and expected activities), then combine that with external data sources. The system is self-learning, the more it is used the more accurate its predictions are.
To the customer, Personetics offers time management, relevant banking guidance, tangible economic returns, money-saving options, etc. For a financial institution, it reduces operational costs, improves customer engagements, cross-product utilization, share of wallets and differentiates it from the competition.
The total amount of investments raised by the company is $18 million.
ZestFinance is one of Los Angeles-based big data companies that has an intention to revolutionize the process of credit decisions making. It uses machine learning and data science in order to make that process fair and transparent.
The company claims that its big data underwriting model provides 40% improvement over the current best-in-class industry score. That translates into more accurate credit decisions, which leads to increased credit availability for borrowers and higher repayment rates for lenders. This new approach to underwriting could enable lenders to expand their customer base, take business from their competitors, and better serve existing borrowers.
This year, ZestFinance has also announced a new product (Basix Loan) targeted to near-prime borrowers whose credit scores are too low to get a bank loan.
“We decided as part of our new and expanding uses of our underwriting platform to launch a product into this underserved market of these near-prime customers who are actually a pretty good credit — they have good jobs good income, etc. — but they don’t have fair and transparent credit,” says ZestFinance chief executive Douglas Merrill.
Recently, ZestFinance has raised $150 million in debt financing from Fortress Investment Group. The total amount of investments raised by the company is $262 million.
RevolutionCredit is one of California-based big data companies providing solutions for credit decisioning process affecting both lenders and consumers. It has created a platform for creditors and consumers to engage and a database of unique consumer economic behavior data.
The above mentioned solution enables creditors to engage with their customers at the point of transaction to acquire unique behavior data.
Using non-financial data such as social media activity and mobile phone usage patterns, big data analytics deliver a quicker, cheaper, and more effective credit assessment of consumers who lack credit histories and were invisible to lenders before.
The total amount of investments raised by the company is $5.7 million.
BillGuard is one of New York-based big data companies providing a kind of virus checker for financial transactions, or a spam blocker for overcharges and fraud. It’s a free service for consumers to use. One enters bank account information, and the system scans your transactions against a database of red flags, much like a spam checker, either from known issues or scams other users have reported.
BillGuard has an intention to empower consumers to control and protect their money.
The company claims that it has helped its 1.3 million users flag more than $70 million in unauthorized charges since its inception in 2010.
The total amount of investments raised by the company is $16.5 million.
This year in September, Prosper (one of the biggest peer-to-peer lenders) announced that it had acquired BillGuard for $30 million.
“Five years in the making, this is THE perfect marriage for both companies,” BillGuard founder and CEO Yaron Samid says in a blog post titled Live Long, BillGuard and Prosper! ”This is the culmination of BillGuard’s founding mission to empower consumers to control, protect and do more with their money.”
Centrifuge is one of Virginia-based big data companies solving data analysis problems in the areas of fraud and anti-money laundering, retail loss prevention, intelligence analysis, cyber security, etc. It uses Visual Network Analytics (VNA) in order to help organizations discover insights, patterns and relationships hidden in public, cloud, social network, and enterprise data. Centrifuge’s approach combines agile data integration, dynamic relationship mapping, and interactive visual analytics to reveal insights in big data. That includes link analysis, social network analysis, interactive visualization, and collaborative discovery.
Roughly speaking it works through pattern-based visual discovery in big chunks of data.
The company claims that its Visual Network Analytics is also used in some of the most demanding applications in the world, including counter-terrorism, homeland defense, and financial crimes analysis.
The total amount of investments raised by the company is $6.5 million.
“We have a disruptively lightweight, inexpensive technology,”Centrifuge CEO Renee Lorton says.
EidoSearch is one of New York-based big data companies delivering predictive analytics to the investment community. It uses pattern search technology to transform massive amounts of data into valuable analytics and projections for investors.
EidoSearch uses a big historical tracker that allows users to quickly search variables and discover relationships, such as stock price and volume.
This year, Bank of America Merrill Lynch has announced the addition of EidoSearch Predictive Analytics to its BofA Merrill Open Minds alternative research platform. The platform complements the firm’s proprietary research covering areas such as corporate integrity risk, federal legislation and regulation. The addition of EidoSearch should expand the specialized research suite currently available on the platform.
“As the founder of First Call and StreetEvents, and former Chairman and CEO of Thomson Financial, I built my career in delivering technology solutions that improve the efficiencies of asset managers and hedge funds through better organization of data,” says Jeffrey Parker, Chairman of EidoSearch.
Digital Reasoning is one of Tennessee-based big data companies developing software that can read and understand text as humans do.
Recently, the company has won the KMWorld Promise Award 2015. It is a kind of quality proof for its Synthesys® cognitive computing platform demonstrating its success in working with clients to embed both technology and knowledge into work processes to achieve positive business results.
“Digital Reasoning demonstrates the true value of cognitive computing technologies and practices through its Synthesys software platform, which understands how humans communicate by analyzing the context, content, and relationships within big data while semantically revealing what’s most critical to customers,” says KM Award lead judge Hugh McKellar.
CEO Tim Estes says the company sees a coming “understanding gap” between human attention spans and the amount of data available, which is increasing at a rapid rate. This would, for example, make emails searchable based on the facts and relationships in them, replicating the human reasoning process.
“It will make useable data that’s unstructured now that is available only currently if humans are in the loop,” Estes says.