UPDATED BY
Matthew Urwin | Feb 07, 2024

How you use data is more important than how much data you have, and the finance industry has taken this reality to heart. More and more companies have begun applying big data in finance to extract rich insights from the wealth of information they have at their disposal.

Big Data in Finance

Big data in finance turns massive amounts of information into actionable insights, using data to predict markets, craft personalized investment portfolios and speed up customer-facing processes.

The rise of data analytics has enabled finance organizations to quickly deliver stock market insights, provide more accurate risk analyses, detect fraudulent transactions and anticipate customer needs. As a result, businesses now offer more relevant services while advising customers on how to reach solid financial ground.    

The finance sector has made major improvements with its data-first approach, and the following companies are harnessing big data in finance to aid in processes like lending, scoring, risk, fraud and more.

 

Big Data Analytics in Finance

One benefit of introducing big data in finance is its ability to discern patterns from large financial data sets. Organizations have taken advantage of this skill to make more data-backed, accurate decisions. From in-depth predictive models to fraud detection methods, financial groups are leaning on data analytics to identify patterns and support their customers with enhanced service.

 

Location: Fully Remote

Enigma takes vast amounts of data from diverse sources and scans it for usable intelligence on privately held small and medium-sized businesses. It then delivers insights on which companies its clients should engage with and which they should avoid. Enigma’s data is meant for use across organizations, and can be impactful in everything from underwriting decisions to B2B marketing for financial institutions.

 

Location: New York, New York

DemystData helps financial institutions and businesses access hundreds of data providers and analyze external data in a centralized dashboard. With an ecosystem of external data at their disposal, organizations can then efficiently profile customers, flag fraud, automate verification and enhance compliance efforts. 

 

Location: San Francisco, California

Flowcast’s artificial intelligence platform helps businesses and financial institutions make data-driven credit decisions and glean important insights from predictive models without the need for code written in-house. Smartcredit, the company’s flagship product, uses a more diverse set of data types to provide more information than other credit scoring and risk models.

 

Location: McKinney, Texas

ScienceSoft offers software and big data solutions aimed at creating a more organized and secure financial sector. Whether clients want to get started with mobile banking or lending software, ScienceSoft assesses each decision with analyses of customer behavior, product performance and potential risks. Clients can then prioritize specific services based on customer preferences, better secure their assets and improve their operational efficiency.

 

Big Data and Financial Risk Analysis

Those who spot patterns within their data can anticipate events before they happen, which is why the finance industry has invested heavily in big data best practices. Companies are leveraging data analysis to conduct more thorough risk analyses, helping investors pinpoint unqualified loan applicants, bad investments and other financial pitfalls. 

 

Location: New York, New York

PeerIQ is a data and analytics company enabling originators, warehouse lenders and asset managers to manage and analyze risk in the consumer credit market. PeerIQ’s platform provides more data transparency throughout the funding chain, connecting lenders with capital markets. It can also help clients validate data from multiple sources and keep track of this data with reporting services, portfolio management tools and cash flow analytics for more solid transactions.

 

Location: Toronto, Ontario

Quandl provides financial, alternative and economic data to thousands of investment professionals globally. Quandl’s platform is employed by some of the world’s leading hedge funds, investment banks and asset managers to glean information from datasets they normally wouldn’t be able to access. Once customers determine which data to extract, they can load data directly onto the analysis tool of their choosing, including Python, R, Excel and Ruby.

 

Location: Burbank, California 

ZestFinance combines AI and big data to support lenders with a management system that builds reliable credit models. Lenders can then gain more accurate insights into the credit backgrounds of potential borrowers and gauge how much risk each individual presents. With more trust instilled in the process, ZestFinance helps lenders improve their approval rates while avoiding potential losses.

Related ReadingWhat Is Alternative Data and Why Is It Changing Finance?

 

Big Data and Financial Accessibility

Organizing data makes information easier to find, and big data excels in this department. Data analytics techniques enable finance organizations to arrange customers’ info in a way that allows them to quickly retrieve that info. As a result, businesses are working with customers to simplify finding digital receipts, securing credit for loans and accessing other financial data.

 

Location: Fully Remote

Tala is a data science company that makes credit accessible to underserved areas around the world. Currently working in Kenya, India, Mexico and the Philippines, Tala users can apply for loans through the Tala app. Most users obtain credit in less than ten minutes. Tala helps users build a digital credit history or financial identity without traditional requirements by using alternative data to approve loans and assist customers in establishing credit.

 

Location: Toronto, Ontario

Sensibill offers receipt management tools for mobile banking. When users photograph their receipts, the Sensibill app extracts and structures the purchase data within them, making the information storable and easily accessible.

RelatedRead More Data Science Stories

 

Big Data and Fraud Detection

The financial sector has always been vulnerable to fraudulent activity, but developments in big data in finance have made it harder for faulty transactions to slip under the radar. Partnerships between big data and machine learning have allowed businesses to build behavior models that single out abnormalities during transactions. Finance groups now follow a proactive approach in snuffing out fraud and taking extra steps to give their customers added peace of mind.

 

Location: Austin, Texas

Oracle’s cloud platform and software tools are used by the finance industry to quickly organize data and detect anomalies. With a focus on big data, the company has found efficient methods for arranging and storing large amounts of information. This has allowed Oracle and its clients to better monitor data sets, so they can locate unusual transactions, file reports and stay within regulations.

 

Location: Fully Remote

Sift Science uses machine learning to help businesses fight all types of digital fraud, from payment fraud and account takeover to content and promo abuse. Sift ensures digital transactions are secure for both the merchant and the buyer. The machine learning platform uses customer data and signals to understand risky behavior and stop fraud before it occurs.

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