Big data: the new transformational tool in finance
The transformational aspects of big data have changed the way in which in numerous sectors operate. Proving a more extensive path for business expansion, the introduction of big data in finance has opened up a new area of exploration for the industry, including the introduction of FinTech.
Ushering financial institutions towards the implementation of new technology, the identifiable crossover between the technologies is becoming more distinct. With many businesses now recognising big data as a vital tool, if you’re still behind the curve, then your business will be missing out.
Big data has grown exponentially in the past decade, with the creation of new systems and algorithms allowing use on a broader scale. Closely partnering this technological improvement is the increasing standard of big data practices and principles which, in the financial sector, you’ll be unable to avoid.
Collecting data is a vital part of the everyday running of financial institutions, and it’s something that’s becoming a crucial aspect in the early detection of fraudulent activity. Previously, the discovery of this type of action would rely on specific identification factors such as unusual login times, bad IP addresses or abnormal transactions.
But now, due to the significant advancements in big data and analytical practices, it can provide you with the chance to detect threats as soon as they happen, utilising real-time analysis to identify any risks attempting to bypass your protection protocols.
As we’ve explained above the prevention of fraud is a vital part of everyday operations in banking, and partnering this is the increased capability of real-time analysis. Used to detect immediate security threats, finding the correct interrelation between the processes can be pivotal when increasing your security protocols.
While harnessing the power of big data can be beneficial, it also opens up new avenues for threats to infiltrate your data, networks and system infrastructure. Having the right team on hand to analyse incoming data will enable you to isolate risks before these threats can break past your security setup, and ultimately protect all the customer information you have stored.
Creating a complete statistical overview and potential threats analysis of your business will allow for the creation of specially designed algorithms. Designed using historical data that shows previous data breaches, each specific formula will lead towards easier identification of new threats.
To use these algorithms successfully, experienced members of your big data team will comparatively analyse all newly collected data that looks suspicious against each formulae and similar historical data. If a security risk is discovered, they will implement the relevant security measure, which will protect against the current threat, and also offer future risk prevention.
Data in abundance
The introduction of big data was born out of necessity, as traditional tools used by financial institutions could no longer cope with the amount of incoming information. This amount of data has established new areas for analytical and security procedures to be improved, and Chris Gledhill, CEO of SeccoAura, believes it’s one significant positive following its introduction to the sector.
“Banks have a lot of data,” says Chris. “They’ve solved the first challenge of where to get the data, they’re sitting on piles of stuff. They’re also rather good at keeping data secure. By definition, banks are secure beings and so have far more consumer trust and moral permission to act upon the data they have.”
Big data has created the opportunity to increase computing power in the financial sector, which has led to developments in the storage and analysis of data. However, the most significant advancement to partner the introduction of big data is the development of FinTech, and the capabilities this now opens up for FIs.
FinTech is still a young industry that is continually changing. It allows for the positive aspects of machine learning and artificial intelligence to be unleashed in the finance sector. It’s from this introduction of new technology that we can identify the beginning of a burgeoning relationship between FinTech, big data, and banks. Financial expert Alex Jiminez believes that if FIs were left to their own devices, they would not be undertaking innovations of their own accord.
“Prior to the rise of mobile, the last truly innovative change in banking was the introduction of ATMs,” says Alex. “At a minimum, FIs have needed to keep up with the Fintech firms by putting the customer in the centre of their activities, as this is something they have moved away from throughout the years.”
Due to the incorporation of FinTech, customers are at a point where all of their account information has become completely digitised. They have complete control over their transactions, a practice that in the past would have been managed across a counter.
The continued financial revolution has also not stopped account management techniques, as the introduction of the digital wallet allows transactions to be completed directly from a mobile device.
This advantageous move from developers allows the use of payment on these devices and has rapidly changed the way purchases are complete and monitored.
A further technology that has risen to fruition is blockchain, and for those who are unfamiliar with the technology, it’s a system that uses a peer-to-peer network of computers to validate transactions. It allows users to make and verify transactions without a third-party central authority.
Offering a system that gives users full control over their financial well-being, it strongly reduces the security threats by providing a more difficult platform for hackers to intervene in transactions, creating a robust system.
Despite big data influencing this technology, and proving to be the catalyst for its introduction, Chris Gledhill believes blockchain has stolen the limelight, even though big data will create a long-lasting impact.
“The biggest influence conceptually has been the shift from batch to real-time data,” says Chris. “Banking was a schedule-based clearing whereas the Fintech demands real-time data. The biggest practical influence has been in the back office with things like credit scoring and risk engines.”
However, there are still developments to be made in Fintech, as it’s still very much in the building stages. While it has already made a positive impact, there are numerous levels of evolution expected to take place before it can become the ultimate tool for FIs. But as Modefinance explains, without the availability of big data, FinTech wouldn’t exist.
“The lending of mathematical and statistical knowledge to the financial sector is the key ingredient to give added value to the data the industry generates,” the company says. “Another plus of FinTech is the development of a strongly connected web in a data exchange for financial data, inducing the standardisation and facilitating the job of supervisory authorities.”
Contrasting the positives associated with the introduction of any new technology is the negative effects, and when implementing big data there’ll be teething problems faced by your business that could put you off completing the process.
The reliance to make decisions
As the use of the big data continues to grow, you will be inundated with considerable amounts of information that, although can validate and guide your decision-making process, can not make the right move for you.
When utilising big data as a decision-making tool, our advice is to use enough data to support or negate the theories you already have, but don’t ultimately rely on the information, as whatever decision path you choose to go down should be chosen by you and not data.
FIs allow data to remain unused
The big data revolution has firmly hit the financial sector. However, with the influx of new information placed in storage silos, the possibility of vital information being overlooked is apparent, with Alex Jimenez affirming that data will remain largely untapped.
“There is a myriad of positives such as using data to drive business strategy, address micro and macro customer needs, competition, control risk and improve operation,” says Alex. “The negatives are that much of the data remains siloed, competition for resources is stiff, and traditional FI management haven’t learned how to use data to manage a business.”
You have to use the correct statistical techniques
When using big data in finance, one element of your business that will be under constant scrutiny is data analysis. As you delve deeper into the information you’ve collected, you’ll discover the increased volume will require you to develop new statistical analysis techniques to obtain the results you require.
Throughout the new data collection process, as more data flows into your business, you’ll discover that it will predominantly be unstructured data, and this is where problems can arise. To successfully obtain the right information from these datasets including patterns, interdependent correlations or potential risk, the team tasked with the statistical analysis of your data will be charged with creating new algorithms and methods to analyse the new data.
This can be quite a time-consuming process, especially if the streams of information flowing into are constant, and can cause added strain to your business, which you may need to address both financially and in staffing costs.
As you begin to explore the avenues opened by big data, you’ll discover a number of different aspects branching from the technology that can help your business to grow and adapt to successful use.
Introducing data mining into your everyday operations will allow you to process more raw data into useful information that can help your business grow while also offering insight. To gain successful insight into what the data can offer, you may be required to use a specific piece of software that will use created algorithms to search for patterns in the large amounts of data your business collects.
The process as a whole will take five steps for you unearth the hidden patterns and information in your data.
- Collect the data and load it into a data warehouse
- Store and manage the information
- Organisation by your data management team
- Analysis and sorting per user results
- Visualisation in an easy to share format
However, when using this technology Bob Katz, CEO of FACTS, advises caution. “These solutions, particularly when used by financial service firms, often involve complex algorithms that represent business or trading strategies,” says Bob. “Unauthorised access to this data, either externally or internally through hacking or re-engineering, can provide insider information and adds another layer of security”.
In the financial sector, the use of big data shows zero signs of slowing down. The constant introduction of new technological advancements has lead businesses down paths they never thought possible and allowed for a rapid increase in business growth and development.
With these advancements expected to continue in the sector, industry experts foresee a bright future for the banking sector, with the use of each new technology offering opportunities for further expansion.
Has your business recently implemented big data? Tell us your success stories in the comments below, and don’t forget to sign up to the Churchill Frank blog.