Group 14 Big Data in Finance Summary

picture3Jacobs University

Course no: JTBU-020003

Course name: Big Data: Big Boon and Big Brother!?

Big Data in Finance

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Abstract

The increase of data volume in the digital universe if tremendously high from 281 Exabyte’s (2007) to 1’800 Exabyte’s (2011). However, a vacuum has been created because large scale adoption within capital markets has been elusive.

Capital market firms have been making changes and improving to handle big data. In this article, an overall view of Big Data in finance will be considered.

Group members                                                         Instructor of the course

Juna Kreka                                                         Prof. Dr. Adalbert F.X. Wilhelm

Lauren Mugabo

Samuel Ngwarai

Milena Viyachena

What does Big Data in Finance mean?

Big Data is usually defined by the 3 Vs (Gartner 2001) and lately a fourth one is added:

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How to use data derived by man and machine for decision making?

The survey was made within industries such as healthcare, banking, communications, energy and utility technologies. 2,100 companies were surveyed. The results showed that:

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  • 48% of the companies that said they are ready to grow in new markets, describe themselves as highly data driven.
  • Executive officers want the decision making process to be faster and more sophisticated, especially in healthcare banking and insurance.
  • The companies need analysis that looks into the future (predictive analysis). But still only 29% use such analysis.
  • 41 of the executive officers say that it is important to use machine algorithms.

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Where the use of Big Data in banking and finance is most common:

  • Fraud detections – by using machine algorithms a customer profile is created (what is the customer usually doing, what transfers, what investments, etc.) and then it is possible to distinguish normal activity from a fraudulent one.
  • Compliance with the regulations – all big deals must be documented and monitored by the regulation authorities.
  • Customer segmentation – by collecting and analyzing data about the different customers, the companies can make different strategies of how to address such customer group.
  • Following all new regulation requirements and informing the management so, it might be better and quicker to react to these regulations.
  • Personalized product offering: by implementing special software the management can follow and understand the customer’s habits, who are the decision makers and thus, offering right products to the right customer.
  • High frequency trading – this is a high volume and low margin (low profit) trading that is done very quickly (buying and selling shares in the stock markets). In such a trading it is very important to receive and analyze big volumes of data before making the deal. By using computer algorithms the data can be processed almost in real time and the expected results are used to take the right decisions quickly and make a bigger profit.

High frequency trading (HFT)

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This is a program trading platform that uses powerful computers to transact a large number of orders at very fast speeds. It uses complex algorithms to analyze multiple markets and execute orders based on market conditions. Typically, the traders with the fastest execution speeds are more profitable than traders with slower execution speeds.

Big data helps in HFT since it provides companies to access all data about their clientele and services in one place and can make decision from them.

Credit worthiness

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This is a valuation performed by lenders that determines the possibility a borrower may default on his debt obligations. It considers factors, such as repayment history and credit score. Lending institutions also consider the amount of available assets and the amount of liabilities to determine the probability of a customer’s default.

The development of big data resulted in services such as Big Data scoring that collects vast amounts of data from publicly available online sources and uses it to predict individual’s behavior by applying proprietary data processing and scoring algorithms. Based on client feedback, their solution delivers an improvement of up to 25% in scoring accuracy when combined with traditional in-house methods. The added benefit can be even greater to people with little or even no credit history, for example: young people, since most of their information is dug up over online platforms and social media they can be able to trace even people who are new in the banking systems from their former lives

Improved call center operations

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Big data helped companies with the use of advanced data collection, data analysis, and artificial intelligence (AI) to improve the call center experience for customers and provide valuable insights for companies.

There was also development in speech analysis which helped companies to be able to determine the age of callers even how they express their feelings and these data were able to used in future production and advertising campaigns.

Commodity trading

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 This is an investing strategy wherein goods are traded instead of stocks. Commodities traded are often goods of value, consistent in quality and produced in large volumes by different suppliers such as wheat, coffee and sugar. Trading is affected by supply and demand, thus, limited supply causes a price increase while excess supply causes a price decrease.

In today’s world companies use real time decision making mechanism that can help them to visualize what are the firm’s most popular products and what they can gain from a certain product.

Investment advice

Big data helps investors access information about the business that were not able to be accessed in the past and this efficient for them to be able to base on them for judging about their prospective investments and what might come out of them.

Customer retention

Companies are always attempting to make sense of the vast amounts of data generated through personal, societal, and industrial internet interactions from social media, mobile devices, geolocators, and digital sensors. Examining these information companies can bale to create strong customer loyalty and retention since these information actually hold everything about their clients and where they are located and what they are popular in.

Key drivers for using BIG DATA in Capital markets.

Big Data has essentially revolutionised how we visualize business and ‘risk taking’. In the past there was almost always a sense of insecurity when releasing a new product into the market where there was no data to let you know if it would be an entire success or failure. This has left big data in a place where large Capital Markets are finding it very important in their everyday business lives.

One of the main reasons for this is that there has been an exponential increase in Big Data and this has made it easier for any firm to make informed decisions on a product. One of the most important data sources are actually social forums like Facebook and Twitter where countless people from anywhere can review their products, complain about them or praise them. Hence large decisions are increasingly depending on how people respond to their firm and products online.

Big data also has the capabilities of helping companies improve their marketing and enhance their customer experiences by knowing exactly which products are most liked at what locations. For example, the coca cola company has publicly said that even though the Coca Cola Classic and some of their other drinks are available in several countries, there are many cases where they “tailor” the “drinks to local tastes”. This has been made possible largely to the availability of big data that was available for them to use to their benefit.

Another key driver for Big Data in capital is the increase in production complexities. Companies now need to make hundreds of thousands of high quality products per day and they will always need to have the right data to manipulate for the best results every time. This in turn affects their productions speed as they compete in markets where working with a stringent timeline means you either finish what you have to do in a day or you lose the customer. Moreover, you will find that the cost of data systems has decreased over the years and now more and more companies are able to afford it. This and open source software also made it possible for almost anyone to take advantage of big data.

Big data in its whole goodness reduces cost of production as a whole and helps management of large financial decisions that for example insurance firms have to make per day. The large amounts of big data available let them know exactly whether you are likely to have an accident if you live in a certain area which in turn makes your insurance higher hence making business less of undirected risk taking for them.

Conclusion

We must know that these are still the baby years of big data as it matures in the business world. Those who have seen what it can do for them have already moved to it and are increasing their profits by accessing information about exactly how much of a certain product was bought at which place for what price all around the world. Largely these are also the early days of big data because most businesses that could also be using it are oblivious of what it is or are unsure of what it can do for them.

Big data has changed several firms to put it at a much higher important place in their companies. This helps them to visualize information that largely helps with regulatory and market infrastructural change, and this also increases transparency and even security as big data also helps detect and flush out frauds and any external but mainly internal crimes against the company.

As the days move by and the next big technological thing comes out big data becomes a part of everyone’s everyday lives. It can come from the smart technology that we seemingly cannot live without for extended periods of time which in their own way act as big data sources over our lives. A single person’s smartphone can contain so much data about a person’s traveling lifestyle, friends and family, health, and even sleeping habits. This is all so much you can to some extend confidently say you know a lot about the actual person once you have been able to go through and analyse the big data that surrounds them.

References

 

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One thought on “Group 14 Big Data in Finance Summary

  1. If anyone is more interested in the high frequency trading (HFT) example, I have found an article from the magazine “Mother Jones” about the dangerous and threatening features of HFT. I think especially the first part is worth reading since it is elaborated on how HFT evolved over the last years, and in which way this can contribute to economic downturn like the financial crisis in 2008.

    However, this is only one view, and since the magazine is often seen to be on the left-wing political spectrum, it is not a big surprise that it argues against complete freedom of trading and markets.
    Still, the possibility of restricting HFT is worth to be taken consideration when trying to minimize crises of the kind that struck us in 2008.

    Article: http://www.motherjones.com/politics/2013/02/high-frequency-trading-danger-risk-wall-street

    Finn

    Like

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