For data streaming just about anywhere, it’s more relevant for what you use it than how much you have. Including strengthening safety and corporate operations to optimizing success in education and athletics, the data to which organizations have exposure is a game-changer in multiple sectors and industries.
Big Data is the collective term used for contemporary technologies and methodologies used to collect, sort, process, and analyze massive, complex sets of data.
Humans are creating data at an exponential rate. In fact, 90% of the data in the world has been created in the past 2 years according to a 2015 IBM study. In the same study, it was estimated that we create 2.5 exabytes (2.5 quintillion bytes) of data every day. To put it in perspective, there are 18 zeros in a quintillion.
As Big Data gets, well, bigger, it becomes even more important for executives and C-suites in financial services to stay ahead of the curve. And data creation isn’t slowing down anytime soon. According to the World Economic Forum, it’s estimated that by 2025, we will create 463 exabytes of data each day, the equivalent of 212 million DVDs each day.
Have you ever wondered how much data we generate? Tones of Gigabytes. Watch this video and find out what Big Data really is.
Quality of data
Finance companies want to do more than just store their data, they want to use it. Since data comes from so many different sources, it does not always accept and poses a challenge to data management. Information processing systems ensure the integrity, reliability and security of the records.
At the same time, real-time analytics tools provide big data stores with exposure, precision, and speed to help companies extract quality insights and enable them to introduce new goods, service offerings, and capabilities.
Many businesses today face strict regulatory requirements such as the Trading Book’s Fundamental Review (FRTB) that govern access to critical data and accelerated reporting requirements.
Some Big Data technology allows financial institutions to scale up risk management cost-effectively, while improved metrics and reporting help transform data for analytical processing to provide the insights required.
Financial data comes from a variety of sources including employee documents , emails, business applications and more. The combination and reconciliation of big data requires data integration tools that simplify the storage and access processes.
Big data technologies and the cloud operate together to meet these challenging business problems to overcome them. As more financial institutions adopt cloud solutions, they will become a stronger indication to the financial market that big data solutions are not only beneficial in IT use cases but also business applications.
Is Big Data used in CyberSecurity? Of course it is! Hadoop, Spark, Casandra are just a couple examples of BD technologies used in this industry.
In September 2017, Equifax reported a data breach that revealed the personal information of over half of Americans. In one of the biggest Big Data scares of the 21st century, 147 million people had their complete identities and extremely vulnerable information exposed. Since then, CyberSecurity has become one of the main Big Data priorities in the financial services industry.
Versive is a company that created software that they claim can help financial institutions and banks analyze massive transaction datasets and cybersecurity data using machine learning.
The software is called Versive Security Engine and is touted as being able to integrate easily with cloud, hybrid, or on-premises data structures. This software uses machine learning algorithms to find patterns and abnormalities in the data networks that could indicate potential cyber threats.
Versive claims they have helped Riaz Invest Limited improve security within customer data. They claim the Versive Security Engine was able to reduce the number of false positives in threat identification at Riaz.
Robots aren’t just replacing truck drivers. They’re making their way into the financial sector and their next victim is the financial advisor.
Robo-advisors are being deployed to offer low-cost personalized financial portfolio advice to customers. Big Data analytics is now being used to passively manage portfolios without human intervention, strictly based on algorithms.
The first real robo-advisor, called Betterment, was launched in 2008. It offered investors access to asset rebalancing within target-date funds to manage passive investments. Even though human advisors already had this type of software, it was the first time investors could use it themselves without having to go through a human advisor.
It is predicted that in 2025 robo-advisors will act as digital platforms providing automated algorithm-led financial planning services without the need for human advisory. Such robo-advisors will first collect client data about their financial picture and their goals through surveys and use this data to automatically invest in assets and offer financial advice.
Simple versions of robo-advisors come in the form of chatbots, addressing simple customer inquiries, walking customers through the sales cycle, offering tips, advice, all while gathering customer data to help improve the customer experience.
According to Barron’s article, digital investment advice is poised to reach $1.26 trillion by 2023 as more everyday investors seek out low-fee robo-advisory.
Having lots of friends on Facebook or Instagram followers means popularity in certain social circles. Nowadays, your Facebook friend list may also impact your creditworthiness.
Neo Finance, Lend, and LendUp are a few of the growing number of credit businesses using personal data mined from social networks like Facebook, Twitter, and LinkedIn to analyze a consumer’s credit risk, according to an article in The Wall Street Journal. These companies believe a person’s social life, professional connections, and digital reputation should be considered as important factors when extending credit.
These startups aim to take advantage of a perceived downside to traditional loan criteria strictly based on FICO credit scores. While many people who lack borrowing experience or missed payments would be disregarded by traditional criteria, LendUp goal is to reconsider loan extensions by looking at people‘s social status.
For instance, perhaps recent immigration to the U.S. or a major medical emergency is dampening somebody’s credit score, while strong, active social ties and community connections may be a perceived indication of low risk. On the other hand, LendUp might find out that someone changed their phone numbers a lot, which is an indication of bad risk.
As Big Data becomes more widespread in the financial services industry, mortgage lending will also face many changes in 2025. Between 2014 and 2017, mortgage industry spending on big data increased from $2.6 billion to $3.2 billion, according to Soma Metrics.
Similar to consumer social credit scores, mortgage applications are going to go beyond traditional data analysis methods and will begin incorporating social media data as well. Big Data will also be utilized in the application process to mine important inputs from public databases, bank records, and other websites to gather as much information on applicants as possible.
Another approach for the mortgage application process is to have homeowners finish their applications as usual, and then use the mortgage company’s pre-populated data to identify any discrepancies between the applications. This will ensure even greater accuracy and reduce the application process time for applicants. Additionally, it will also help determine the risk of identity theft. If there are too many discrepancies, applications could be flagged for further manual reviews with the applicants.
What is more, computer programs will also score applications using machine learning algorithms. Just as resume applications are now commonly being screened by artificial intelligence, mortgage lenders can use it to their advantage.
Based on the algorithms set up, applicants can be approved or denied immediately. Approved applications may be processed right away, while rejected applications may either be discarded or qualified for manual review. This will save mortgage lenders both time and money since human review will not be needed until applications pass through the initial screening. It will also reduce delays in the process, allowing for mortgage lenders to scale easier, reaching more clients.
Big Data analysis is also being used to price properties accurately. JPMorgan Chase is already using Big Data to accurately determine residential and commercial property sales prices that have been repossessed due to defaulted mortgages.
Thanks to combining data on local property markets, economic conditions, and information extracted by algorithms we will be able to recommend reasonable sales prices before any mortgage loans begin defaulting. With more accurate pricing any local property market shakeups from repossessions or defaults should be reduced. Also, there should be a reduction in the time period that a bank is forced to hold a property before making a new sale.
As Big Data becomes more advanced it can also be used to optimize protection and mitigate risk.
Liability analysis functions can provide earlier warning signs of potential risk exposures. Financial institutions are able to be proactive in working with customers to limit liabilities and exposures to provide greater protection. Advanced customer data, transaction data, and geospatial data combined with advanced data analysis that looks at transaction anomalies will allow for easy risk detection and fraud prevention.
Ayasdi, an artificial intelligence software company, uses big data analytics through Ayasdi’s Model Accelerator (AMA), to help financial services companies model and predict regulatory risk with machine learning. They claim their software is able to help banks with regulatory compliance in anti-money laundering, which can automatically scan customer transaction data to reduce false positives and spot any anomalies in fraud detection.
Financial institutions and banks can easily integrate the software into their own enterprise data networks. The software’s algorithm will search through customer data and sales transactions to compare and test risk-models, including the probability of default, loss-given default, and other models. Then, the data insights can be easily viewed in a simple dashboard to discover current risks and predict potential risks.
In times past, large financial organizations that span their wings throughout different departments (retail banking, commercial banking, asset management, etc.) had to set up individual Big Data analytics platforms. This has made data mining and data transfer between the different business sectors extremely time-consuming and exhaustive.
However, in 2020, unified data analytics platforms will make it possible for large financial institutions to benefit from an easy-to-use data analysis system.
A big European financial group serving 40 million clients has recently hired DevsData & GetInData, a world-class team of software and data engineers, to build a unified solution for data processing across departments in 2019.
The client has been a leader in the financial services sector due to its advanced data processing and analytics. However, like most financial companies that have various business departments, it is very complicated trying to communicate different data points between various business units, as each uses a different analytics platform. With retail banking, commercial banking, direct banking, investment banking, asset management, and insurance services, there is a vast amount of data that has to be mined through different platforms.
The unified analytics platform will allow for customized data science environments to be created on-demand. Data scientists will be able to use their own personalized, dedicated work conditions using a simple user interface to create and deploy machine learning easily throughout the entire organization.
The platform will also make data management easier and ensure higher data quality. Data quality checks will be automatically implemented in Spark, resulting in less ingestion to speed the system up. On top of that, Apache Atlas will be used as the core component for data governance actions including data discovery, data tagging, business glossary.
The company is in the process of creating the next-generation, cloud-ready, unified Big Data Analytics Platform that will be based on an open-source stack. It will be one of the most advanced tech endeavors across all business departments whose primary industry isn’t technology.
Big Data has rapidly made its way into the financial services industry as one of the most important roles in business optimization. However, as it turns out, the industry has been lagging behind, big time, compared to e.g. Media or Telecom.
Morgan Stanley’s Digitalization Index compares the top 34 sectors In terms of digital maturity, analyzing how far various sectors have developed within the digitalization path. Out of 34 sectors, you would imagine that financial services would be one of the top industries. But it ranked 18th, behind Utilities, Pharma, and Oil & Gas.
The financial industry has ever-increasing competition in a new era of Big Data. Personalizing the customer experience through robo-advisory, increasing cybersecurity to mitigate data breaches, and looking beyond credit scores into social scores are all at the forefront of Big Data changes in the financial sector.
Leading your organization from the front to compete in this new Wild West of Big Data is essential if you want to stay ahead of the pack.
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