Big Data Analytics Financial Services – Data can bring whole new insights into investing or giving customers a real advantage when they use a service
Deep in the world of finance, the first data-sniffing entities are growing and developing. A lot of data is flowing through your mobile phone. Every digital action sends a cloud of information across the network. Data flows and, in finance, these signals can be a useful snapshot of market activity.
Big Data Analytics Financial Services
The antennae of these creatures are tuned to a wide range of sensory equipment; drones filming plant growth, car activity and high street shoppers; vast electronic “ears” listening to the clamor of social media; indicator of stock market price movements.
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“Two laptop computers occupy an entire floor that used to be full of traders,” said Bartt Charles Kellermann, chief executive of hedge fund consultancy Global Capital Acquisition. “This trend is accelerating, the efficiency of these machines is increasing and investors are seeing the writing on the wall.
“They know that at some point they will convert a lot of their allocations to machine-driven strategies, because they don’t have emotion, they can’t get out of the wrong side of the bed. They sell when they sell, they buy when they have to buy.”
This does not mean that humans are out of the picture. Behind every good trading system is a good data scientist. Being data literate, data scientists understand what particular data does and does not represent. They need to find the right data, check it for quality and then create a model that reflects the reality of the market.
If the data is a constant flow of rich information, it can be the equivalent of a satnav in market negotiations.
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, the investment bank J.P. Morgan noted that the new data sets are often larger in number, with greater speed and variability compared to traditional data sets such as daily stock prices.
Among the alternative data sets noted to be used to guide investments, include data generated by individuals on social media posts, product reviews, search trends and more; data generated by business processes; and data produced by sensors.
If the data is a constant flow of rich information, it can be the equivalent of a satnav in market negotiations. Although in many cases, that data is more like the eyes of the cat in the street, with a small and unreliable distribution, not complete enough to base a decision on.
In those cases, companies may need to go out and look for data sources that can be used to piece together a more complete picture. AXA Investment Managers has already done this, developing its own internal tools to support traders and clients, aggregating fixed income market data.
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Paul Squires, global head of securities trading and finance at AXA Investment Managers, said: “We want to create an environment where the data provided by our trading partners becomes meaningful beyond normal market interaction. We want we give them an incentive to give us data we can respond to, to develop a better dialogue.
Currencies and many stocks trade regularly, but bonds trade more rarely, which makes it difficult to find accurate information about prices, and where to buy or sell them. Combining the use of multiple data sources with human trading skills can provide a real advantage in trading at the right price and executing a deal at its best.
“Data is a key difference in your ability to sell a bond at the right level, but if you’re only looking at data, you’re going to hit a lot of trouble,” Mr Squires said. “Frankly there is no price discovery until you ask a counterparty to price you, so having a pre-trade picture of where a price is is just part of the price discovery process. Get it -phone and get a solid price to sell to another.”
There are also limitations to the existing data as Mr Squires said any stress situation in the market would render the data meaningless. “For bonds, especially, if you have, for example, five million to sell and nobody wants to buy them, there is no price – it’s as simple as that,” he said.
Uses Of Data Analytics In Accounting And Finance
Data is not just a technology game, although investing in the technology to analyze it is important. Whether a retail bank, an investment bank or a fund manager, companies that are good at incorporating the idea of data as an asset in their business. This ensures that the data is always searched and collected in such a way that it retains its value.
“This is less a technical issue and definitely a cultural issue,” said MatthiasKröner, co-founder and chief executive of German digital bankFidor. “It’s a matter of nature what you want to do with it. I can use data in the sense of managing people on the one hand, but on the other hand, I can use it to be easily accessible to my clients the data and easily get the result of the data algorithms in a way that really helps in their financial life.”
As technology advances, what can be considered useful data changes. The analysis of graphics and written words as unstructured data, as opposed to the structured rows and columns of numbers kept in databases, creates new opportunities. Satellite photography and Twitter give all the support marketing ideas.
“It’s all about unstructured data; he finds the data, cleans it, puts it into some kind of analytical framework where the PhDs can then make heads or tails,” said Mr. Kellermann. “There is some doubt as to whether this really adds value; a lot of social media data, people have two views on. Sentiment has an impact, but it is closer to an event. Therefore this type of data is a very sophisticated animal and you must understand many of its dimensions to assess its true value. profit or more efficient operations. No technology offers more promise for delivering new sources of ‘ value to businesses from advanced data analytics.
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In particular, the use of data analytics in accounting and finance is a key factor in increasing profitability and reducing the cost of doing business.
According to a 2020 survey of accounting professionals by software vendor Sage, 44% of accounting firms are using advanced and predictive analytics using big data, or plan to do so in -next 12 months. Among emerging technologies, only 5G has a higher adoption rate among accountants (46%).
Advances in data analytics are creating opportunities for accountants and finance professionals to offer higher quality services to their business clients in three areas:
Data analytics in accounting uses advanced techniques to help companies make the most of the vast amounts of data they collect. The aim is to create value and progress through the use of three new technologies:
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While many accounting and financial services firms plan to use data analytics and other new technologies, the rate of implementation remains uneven, according to the Institute of Management Accountants. For self-service reporting, 48% of companies have completed implementation, while 31% plan to implement. In big data, 32% have completed implementation and 35% plan to implement. For data visualization, 27% have completed implementation and 37% are planning to implement. In predictive modeling, 25% have completed implementation and 33% are planning to implement. For automation, 22% have completed implementation and 42% plan to implement. In voice recognition, 5% have completed implementation and 20% are planned to be implemented.
The fields of accounting and finance are being transformed by new technologies. An international survey of accountants carried out by Sage in 2019 found that 90% of respondents believe there is a change in accounting culture. Changes can be seen in hiring practices, business services, and industrial approaches to analytics, artificial intelligence, and other new technologies.
This transfer includes the opportunity to expand the services that accountants and financial professionals offer to their clients and improve the quality of the services they currently offer. The changes affect many areas of accounting and finance:
One effect of the change in accounting and finance culture is that companies are increasingly recruiting candidates from non-traditional backgrounds, according to the Sage survey. This change is an attempt for accountants to better represent their clients and for accounting firms to add a wider range of skills that they can leverage to serve their clients in business.
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Any business process that collects customer data must ensure that any use of the data protects the privacy and other rights of customers. One of the new ethical problems related to AI-based algorithms in particular is the lack of consensus when systems produce private data that did not exist before. An example is an algorithm that automatically links a person’s bank account activity to location tracking and call history collected from the individual’s cell phone.
The Digital Analytics Association has developed a code of ethics for web analysts that emphasizes honesty and personal responsibility for protecting privacy, operating transparently, and educating users of the web about their work. The analytics service Blast Analytics offers a code of ethics for data analysts that outlines eight ethical guidelines for the profession:
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