Feb 10, 2023, by Paola Reyes
Finance
One of the uses of big data in finances is fraud detection/Cybersecurity. Bank companies get big data every minute from transactions, login information, online credit card limit request, and others (Schmelzer, 2021). The big challenge is to identify what transactions are truthful, and here is where they use machine learning (ML). For example, American Express Bank uses ML to identify unusual activities from their card members (Rachelle, 2022).
Some of characteristics of fraud detention/Cybersecurity’s big data are:
Volume: The collection of the members’ activities contains a huge amount of data. Just having in mind that a Bank such as American Express has 121.7 million cards globally is an indicator of the big data that bank companies are collecting (Zippia, 2021).
Variety: Refers to the types of data of those collected activities (Moore, 2021). For example, the records of transactions can be collected as row data, while the login information can be saved by GPS location, and through image/ video when the member is using an ATM.
Value: The great value is protecting their members’ money and personal information from fraud. The speed of having the members’ activity data is the key to fraud detention in real-time. The faster the bank system can have access to suspicious activities, the faster it can be informing the card owner. Also, creating a good reputation for the bank will increase the number of people interested on become a member of the bank.
Medicine and Health
Machine learning models helps to predict how likely is a patient to develop a disease based on symptoms or attributes such as age or previous health conditions (Coursera, 2020).
Some of the characteristics of big data in the prediction of developing a disease are:
Volume: to be able to create a machine learning model, the more records of a variety of patients and attributes, the better will be the performance and accuracy of the prediction.
Veracity: This characteristic has high importance in the medical and health, because if the data that is used for the model is not true (it has mistaken or it has been altered) would produce a model that is not correct. It can take a patient from taking the wrong medication to getting surgery to treat a disease that they do not have.
Visualization: For scientists, and doctors, it is helpful to have access to visualizations such as charts or graphs that shows tendencies or a large amount of data.
Value: Thanks to the work of doctors, scientists, and machine learning, patients can get treatment on time, and doctors can take measures to prevent the development of a disease, such as bronchitis. Nowadays, if a kid has a severe cough that is severe or last longer than three weeks, has a heart or lung condition, and the season is winter, there is a high chance that the child has bronchitis (NHS, 2023). We can know this, thanks to the collection of big data & machine learning and, the humans behind it.
Military
The military uses big data in its 4 disciplines: Human, geospatial, signals, and open-source intelligence. For HUMINT (Human Intelligence), they gather information by contacting other persons. All this data is collected in various forms such as photos and documents (Abadicio, 2019).
Some of the characteristics of big data in HUMINT are:
Volume: All the information gathered in different formats for each investigation or mission, which keeps growing every day as more data is collected.
Variability: The data obtained from HUMINT is collected and storage in different formats such as video, audio, text, photos, maps, or images (Abadicio, 2019).
Value: The faster they can process the data, the faster they can send it to the commander, which allows them to make better decisions and be able to react faster (Raytheon T, 2018).
References
Schmelzer, R. (2021, April 29). 8 big data use cases for businesses and industry examples. TeachTarget. https://www.techtarget.com/searchbusinessanalytics/feature/8-big-data-use-cases-for-businesses-and-industry-examples?amp=1
Rachelle. (2022, Sep 22). American Express: Fraud Investigation and Prevention. Ecusocmin.https://www.ecusocmin.org/american-express-fraud-investigation-and-prevention/#:~:text=American%20Express’%20real-time%20fraud%20detection%20technology%20employs%20machine,card%20fraud%20by%20several%20different%20layers%20of%20security
Zippia. (2021). American Express Statistics. Zippia, the career expert.https://www.zippia.com/american-express-careers-566/statistics/
Moore, M. (2021). The 7 V’s of Big Data. Impact. https://impact.com/marketing-intelligence/7-vs-big-data/#:~:text=Variability%20is%20different%20from%20variety,impact%20on%20your%20data%20homogenization
Coursera. (2020, October 20). Big Data in Health Care: What It Is, Benefits, and Jobs. Coursera. https://www.coursera.org/articles/big-data-in-healthcare
NHS. (2023, January 17). Bronchitis.NHS Inform. https://www.nhsinform.scot/illnesses-and-conditions/lungs-and-airways/bronchitis/
Abadicio, M. (2019, May 8). Big data in the military – Preparing for AI. Emero. https://emerj.com/ai-sector-overviews/big-data-military/
Raytheon Technologies. (2018, October 3). FoXTEN Intelligence Platform. [Video]. YouTube. https://www.youtube.com/watch?v=ECWsb12vCeg&t=94s
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