Financial institutions are very aware of the perils and pitfalls of rogue data affecting the quality of their data in every area of their organisations.
Fintech describes those companies which are part of the financial world and use the latest data-driven technologies in order to achieve the best results. There is considerable variation in the way different organizations use fintech to advance their business and offer the best services and products to their customers alongside the best tools for their employees to be able to serve said customers in the best possible way.
Data quality is the critical component needed for fintech to work effectively. Which is why using Spotless Data's machine learning filters should be placed at the point of entry of the data repository of any fintech organisations. Given that all financial institutions, at least in the UK, are legally required to have a single customer view for each and every client on their books, almost all modern financial institutions are fintech, and thus in need of data quality solutions to ensure their particular financial institution stands out among its competitors while at the same time ensuring compliance with all the laws and regulations which they are obliged to adhere to and which they often spend considerable time and resources on. Businesses also use fintech to reduce costs, for instance replacing human bank tellers with ATM and similar machines to do these tasks automatically.
Many financial institutions have vast quantities of big data which they need to analyse, much of it full of corruptions, mismatches, duplications and other inaccuracies, making said data ideal for cleaning by using Spotless Data's unique web-based API solution to ensure excellent data quality which you can trust. Hedge funds, insurance companies, banks and other financial organisations now require these big data insights in order to remain competitive when buying or selling stocks or managing risk and uncertainty, given that even if they don't analyse their big data their rivals will do so. These big data analytics tools provide the business intelligence needed to make decisions and to ensure that the financial institution is understood by its owners and employees as well as being the best defence to protect against possible fraud, actual or potential.
In the past, highly intelligent hedge fund and bank managers and actuaries read and studied the data available to them and made the vital decisions which affected the success or failure of their companies based on their understanding of these data and the insights offered by them. Whether or not they made good or bad decisions, their competitors were in exactly the same boat of being reliant on the talent, skills and hard work of their managers and actuaries, and so the best guarantee of being one of the winners was to ensure that the best possible staff were hired, which goes a long way to explaining the sometimes controversial pay packages and bonuses those in the upper echelons of financial organisations may receive.
However, in more recent years the data have simply become too big for individuals to be able to grasp the bigger picture without the help of business intelligence based on data analysis and supplied by software and algorithms in the form of machine learning and artificial iIntelligence. It would take thousands of hours a week to read all the information required to enable a hedge fund or bank to pursue the best possible strategy for buying or selling of stocks and shares while hedging their bets, or for an insurance company to decide what rates of insurance to charge, or for a bank to agree to a multi-million dollar loan.
This intensive studying of big data is well beyond the capacity of one person or even a small group of people while a larger group of people will find that while it could, in theory, do the required research, to then try and reach a consensus decision based on the partial understanding of the data gained by a number of people is extremely difficult. If they only have a few hours or perhaps a few minutes in which to make a critical decision that could cost their company millions of dollars if they make the wrong one it becomes all but impossible for humans alone to draw the correct conclusions.
The advantage intelligent algorithm-driven computer programmes have over human beings is that they can speedily analyse the data they have and make decisions which will allow the companies for which they "work" to out-compete rival firms which only use humans to make these decisions, or use less skillful programmes, or who are working with inherently poor quality data from which to make said decisions, having failed to clean said data.
However, it is still better to manage a hedge fund or an insurance company or other financial institution using human employees rather than artificial intelligence and machine learning programmes if the data said programmes have to work with are poor in quality. Highly educated and well-trained humans are normally smart enough to spot a problem if the data which they work with is suspiciously poor quality. AI programmes, though, unless they specifically have a data quality component implemented into them in order to clean and ensure the quality of the data they ingest on a regular basis, are unlikely to be able to spot any anomalies which in reality are caused by poor data quality.
Currently the two options for this are to employ data scientists in order to build this data quality component, or, much the simpler and more cost-effective option, to build Spotless into the fintech Artificial Intelligence and Machine Learning programmes so that the big data they are analysing are always properly scrubbed up and can be smoothly seamlessly be made sense of. Another alternative is to simply send the data manually to Spotless using one of our subscription packages to ensure quality data at the speed of business before allowing the Machine Learning software to begin its analysis.
The best place to get started with Spotless Data Quality API, and an outline an introduction to the process of using our API. You can also test a file, just scroll down the home page to where it says "Add Your Data File" and press the browse button. You will need to sign-up to do this, which you can easily do, setting up an account using your email address, Facebook, Google or GitHub accounts. You can also view our videos on data cleaning an EPG file and data cleaning a genre column which explain how to use our API.
In order to allow you to test our API, we are giving away 500Mb of free data cleaning in order to allow you to try our API and see how well it works for you. We guarantee that your data will be secure and will not be available to any 3rd parties during the period of time it is in our care. If we find any problems with your data or for whatever reasons are unable to automatically cleanse them to our high Spotless standards which ensure spotless data quality you can trust in, an automated flag alerts our team of data scientists who will then review the issue manually and, if necessary, contact you via your log-in details to discuss the problem.
Here is a quick link to our FAQ. You can also check out our range of subscription packages and pricing. If you would like to contact us you can speak to one of our team by pressing on the white square icon with a smile within a blue circle, which you can find in the bottom right-hand corner of any of the web pages on our site.
If your data quality is an issue or you know that you have known sources of dirty data but your files are just too big, and the problems too numerous to be able to fix manually please do log in and try now