One could say that applications for Data Science are unlimited. At least, as long as you have some quality data to work with. Any type of activity that will allow you to collect data and formulate meaningful questions is suitable.
Especially in Banking and Finance, Data Science has enormous potential. While large companies are building dedicated in-house teams, others are slowly starting to recognize the value and get external services. It also needs to be said that even for banks with established Data Science departments, there is a great benefit in seeking new ideas and inputs from the outside perspective. At Profinit, we care about the right Data Science strategy of our clients as well as delivering proved solutions end-to-end.
Our experienced Data Science team can guide you along the whole data journey. There is also a set of enterprise-ready modular solutions we have developed based on banking-data research. Download our whitepaper about Profinit’s Data Science algorithms! Check out our services.
data science in banking
Data Science in Banking and Finance has enormous potential, which is gradually being mined. While big household names have already recognized this and have large in-house teams dedicated to the task, others are slowly starting to recognize the value and buy it externally. It also needs to be said that even for banks with established Data Science departments, there is a great benefit in seeking fresh input, new ideas and an outside perspective externally. At Profinit, we offer just that.
Our Data Science team can guide you along the whole data journey, but there is also a set of enterprise-ready algorithms we have developed with banking data that you can buy from us. Download our whitepaper about Profinit’s Data Science algorithms!
Data Science plays a crucial role in current risk management strategies and concerns all of its aspects. Predictive models influence the following areas: operational risk, event risk, customer risk, but also credit and market risk (and potentially more). A good understanding of past data leads to sound models and high accuracy of any predictions. As a result, this positively influences risk management in banking and also the insurance sector. Read about this relevant use case and our Household Detector – a powerful tool for banks to mitigate client risk.
Financial fraud is another example of successful Data Science applications in Finance. Based on specific features of a transaction or a client’s profile, algorithms can determine the probability of activity being fraudulent very well. Nowadays, fraud detection is becoming a rather complex field dealing with Early Warning Systems already operating on the data warehouse level, all the way to personal authentification of the client. Data Science provides another tool for analyses of patterns, and much more.
Finally, applying Data Science techniques in Marketing is crucial for efficient business efforts across all industries. In banking, account transactions can be gold in trying to understand underlying customer demographics and needs. Moreover, customer segmentation allows for a narrowly customized experience with, e.g., online/mobile banking and website interaction. In this way, a good Data Science team can deliver you invaluable information that will bring your customer satisfaction to another level. At Profinit, we have developed a set of algorithms specifically for these purposes. You can read more about them in our whitepaper. Download it here.