Data per se has grown to be an asset in varying industries, never more so than in the financial services sector.
When it comes to making informed business decisions, forecasting, revenue management, and risk management, this resource is vital, it’s not the organisation with the most data that will win, it’s the one that knows how to interrupt it, and how to utilize it that will succeed. Over the years, we have seen its evolution and in more recent times, it has come in the form of synthetic data. This type of data is known as annotated information that is generated by simulations that can take the place of actual data, and it has become an alternative for those that would like to advance their studies for a lesser cost whilst keeping the accuracy of results. While this new breed of data has been around for a couple of decades, its more refined version has become an even more integral part for many financial institutions over the past 24-months.
Although synthetic data is artificial and generated to replicate readily available real-world data, some research studies have shown that the results gathered from using synthetic data for artificial intelligence (AI) model training provide enhanced results compared to actual data from events, objects, or people. It has also been noted that synthetic data is necessary for further development of learning and other potential use cases in the area of risk management or risk modelling.
Another benefit of using synthetic data is the protection of data privacy. This key driver is deemed to be most important in the cases where financial services are concerned, and it also removes the possibility of a bias through a more diverse data set. One such use case within the banking world is the use of synthetic data within artificial intelligence models to detect fraudulent activities.
Delving deeper into the details, synthetic data has also improved in many ways, especially for the use of risk modelling. The latest version offers an even more precise outcome that ensures data privacy, the removal of the possibility of the data being processed and reverse engineered, and ensures all regulatory laws are complied with. Using this sophisticated new version provides institutions with the hopes that leaks, loss of data, and stolen data can eventually be a thing of the past.
So, how do we really create this new version of synthetic data? This is done through artificial intelligence and machine learning algorithms, and tailored simulation and dimensionality reduction modelling. In this process, data is replicated using varying attributes of actual data – be it consumer data or transactions, without any of it being traced to the original source. Another advancement in this field is what risk managers refer to as a generative adversarial network, a data generation process that can take over the creation of parameters and guidance of a human in the creation of synthetic data. The usual process captures the different behaviours of consumers and proceeds using a “trial-and-error” method, generating data that provides a look into more complex realities and situations that may not be uncovered through the use of actual data, manual processing or previous techniques for synthetic data simulation.
With all of these in mind, the 4-Xtra Technologies team has developed a Synthetic Data Generator which provides models suitable for the use of various industries. These include environmental forecasting, transport, medicine, health and biological applications, logistics planning and the finance sector.
Additionally, the synthetic data generation solution can be married with the extreme forecasting capabilities to provide extreme event forecasting to unseen scenarios.
While there is a wide range of possible applications in the above-mentioned industries, the 4-Xtra Technologies team is focusing its synthetic data generation efforts on financial services. This is due to the fact that the largest gap in data is believed to be in this industry, and Banks and Financial Services organisations are under pressure to grasp technology innovations that are being driven by three core factors:
- The rapid increase in regulatory and government pressure, controls, and standards.
- Emerging customer needs and expectations due to the evolution of on-demand and readily available services and information, also coined as the “Uber-effect”
- An uptick in new business models that are based on fee-based income instead of interest fee arbitrage.
The 4-Xtra team is banking on their unique approach with this Service-as-a-Software (SaaS) program to assist the growing need for the generation of synthetic data.
While there are many benefits to using synthetic data or synthetic data generation, allow us to summarise the key benefits of using synthetic data by enumerating them below:
Cost Efficiency – The production of synthetic data costs significantly less as it requires less manpower and a shorter period of time. As data has come to be known as the “oil” for the machine of risk management, it has also gotten a reputation for coming at a high price. Replicating existing data through simulation modelling gives institutions an advantage in this aspect. This is made more relevant with 4-Xtra’s model as they ensure the high costs, complications of installment, and difficulty of use is eliminated through their Software-as-a-Service (SaaS) program. With 4-Xtra’s SaaS program, the cost is drastically lessened as they eliminate the need for prolonged data gathering and processing being done by multiple risk managers. Through this program, only an experienced risk manager is necessary to guide the generation of synthetic data.
Diversity of Data Sets – While actual or real-world data remains to be the base of its synthetic counterpart, the latter proves to be able to provide a wider range of data sets available for use and address varying studies and models. This is because synthetic data also gathers the complexities and probabilities, resulting in a rich, diverse, and clean data sets. This gives risk managers a better chance at going through all possible data sets needed for their study, which includes the possibility of creating edge cases to run tests and find out if current models or methods, and systems are performing at the level that the institution requires.
Wider Range of Results – As an effect of the previously mentioned advantage, the diverse range of data sets in return provides a wider range of results. This gives risk managers the foresight necessary to determine all outcomes that they should anticipate and prepare for. It is believed that having this level of advantage allows institutions to remove the limitations as compared to the manual and traditional methods of data generation.
Time Management and Efficient Results – In relation to the initial advantage of cost efficiency, time management goes hand in hand with that. Previously, data generation and gathering take up many man-hours and prolonged studies to be able to come up with data, that of course also translate to more cost. With this advancement, a larger range of data can be generated and processed in a fraction of the time. All of this while maintaining precise and more accurate results. This also leads us to the last key benefit but is definitely not the least – data privacy.
Data Privacy – This benefit, though it might be last on the list, is one of the most important to institutions, be it financial or not. Whenever actual data is being used for any kind of study, there is always a big risk of leaks and the data falling into the hands of the wrong people. With synthetic data, artificial intelligence can identify and replicate the complex attribute of real-person and transactional data, without compromising the personal identifiable information (PII) of the client or clients involved. This type of security prevents data from being stolen as well.
Generally, the increase in use of synthetic data generation through artificial intelligence will allow financial service institutions to proceed with more efficient processes, accurate outcomes, and forecasting, improved risk management, better ways for their know-your-customer procedures, the possibility of removing the risk of data privacy by 100%, eliminating the probability of stolen data and data breaches.
With 4-Xtra Technologies, all of this can be attained by institutions in the financial services sector through the availability of their Software-as-as-Service (SaaS) scheme. Conscious of their effort to provide an efficient yet reasonably priced program, 4-Xtra has addressed the usual concerns in its design and architecture. These include the need for cost efficiency and ease of installation through a reasonably priced interface that can easily be navigated. The software is deemed as easy to navigate with the help of artificial intelligence – its back-end has been gone through several trials and has been tested to ensure that it will be user-friendly for clients.
Through this SaaS solution, clients need not worry about expensive installation fees, complicated interfaces, integration of artificial intelligence to risk management software, and difficult configuration schemes.
To enquire or learn more about this service and program, please contact our team through email firstname.lastname@example.org and we commit to answering your enquiry within 3-hours