Possibilities of Data Science and A.I In Banking

Possibilities of Data Science and A.I In Banking

From the 1990s (considered the Stone Age of Artificial Intelligence) A.I and predictive models have successfully gained traction. From selling very specific credit cards, to reward programs based on the spending, usage and other data from customers. With big data, data science, machines coupled with precise artificial intelligence algorithms will play an imperative role in decision support in the banking sector. What is the future of this exciting tech in the banking sector? Are the possibilities endless or are the limitations clearly evident for A.I in banking?

A.I in Banking Applications

Generally, A.I in banking is relatively new but is based on big data manipulation and mining. Specifically, taking advantage of specialist analytics professionals. For instance, Business Intelligence Analysts, Business Analytics Advisors, Data Specialists, Data Mining Experts, Research Analysts, Big Data Analysts, and Data Scientists. Additionally, Investment banks are the main players focusing on hiring software engineers, artificial intelligence experts, and data scientists. In retrospect, statistical methods, business knowledge, quantitative skills, data science expertise, logical thinking, are imperative when dealing with A.I in banking.

Big Data considerations

Specifically, for financial institutions, the use of data science techniques offers a tremendous opportunity to stand out from the competition and reinvent the wheel. Recently, the Data Science Conference 2019 chose to focus on financial tech(fin-tech) industry, researchers and practitioners. Majorly discussing the immense possibilities of big data and A.I in banking. Though possibilities are only limited by bankers imaginations. For example, A.I in banking has applications in credit management to detect fraud signals in real-time. Furthermore, Big data analysis has applications in advanced prevention and elimination of internal/external fraud hence reducing its associated costs.

Structured and Unstructured Multi-Channel Data

Generally, with big data, banks can leverage both structured and unstructured multi-channel data. This data can be used to inform multiple decisions and trends. For instance, bank visits, customer call logs, web interactions, transactional data such as credit card histories, and social media interactions. Such in-depth financial data mining and analytics is critical and permeates into key business areas, like, business processes, client support, workforce, and risk and reputation.

Machine Learning Algorithms

Furthermore, it informs the development of machine learning algorithms that can analyze the impact of certain financial and market trends through trainings done on customers’ historic financial data. Specifically, new data analytics innovations enable financial institutions to develop A.I systems that are smart enough to learn on the go, automatically refine their algorithms, and improve their results over time. Generally, Data is the life and blood of A.I in banking and has become a powerful tool. A.I in Banking has several smart implementations which I will hint on below. From expert insights, and enterprise use cases, let’s discuss some of the applications.

Smart A.I in Banking Implementations

Feedzai, a leading data science company prevents various fraudulent activities in online and personal banking. Specifically, Feedzai uses a unique machine-learning algorithm to prevent this type of fraud. Basically, combining statistical data analysis with a consulting approach, artificial intelligence, machine learning, and modelling tools. Additionally, Mckinsey offers Panorama, a fin-tech application that combines banking and financial data sources for predictive analytics. Additionally, Mckinsey helps private equity firms and banking services to identify which global markets they may be seeking and which financial technology companies they can invest in.

More Fintech Implementations

Axtria and Teradata provide big data analytics used by financial services companies to automate financial and accounting processes. Additionally, minimize financial fraud and cybersecurity risks, and enhance the customer experience through machine learning. Particularly, Axtria avails a cloud information management service to help clients create new data sources that help them target the right customers. Or motivate their sales force to increase productivity, and streamline reporting.

Likewise, Cyfuture Analytics uses rigorous algorithms and the latest fin-tech technologies to deeply analyze financial data. Furthermore, Datarobot helps financial firms create accurate predictive models. Thus, improving decision-making on issues such as fraudulent credit card transactions, digital asset management, direct marketing, blockchain, and lending. Such Banking analysis of transaction data allows identification of risks based on simulated market behaviour, customer and potential customer ratings.

Conclusion

Lastly, A.I in banking will grow in the coming years. Simply because the largest banks have huge amounts of historical data about customers and transactions that can be fed into machine learning algorithms. Eventually, A.I technology will quickly mimic the thinking process of human analysts to review every transaction in each bank’s portfolio. Furthermore, Intelligent trading systems will deeply monitor both structured (databases, spreadsheets) and unstructured (social media, news) data in a fraction of the time ordinary people would need to process it. So, opportunities are limited by the financial mind in essence.

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