Predictive Analytics Has Become a Reliable Source For Enhancing Future Estimations in Banking Domain

15 Sep
2020

 
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The involvement of IT & Artificial Intelligence (AI) in banking sectors has resulted in several transformations. One of the best example is, the adoption of predictive analytics in banking sector. Predictive analysis with the help of a computer model helps to predict the future. The financial institutions can use databases and historical transactions with the help of smart computers, and predict future events to enhance services and abilities. Moreover, predictive analytics has come out as a cost-efficient and improved solution for the banking sector.

Analytics helps financial institutions to help customers manage their accounts and rapidly finalize their banking tasks. It also decreases the risks regarding banking activities. On the other hand, it offers personalized solutions. In addition, it offers easy-to-access products and enhances customer experiences in regards to security and privacy. Predictive analysis has fulfilled both customer demand as well as banking goals, with its numerous features and modernization.

The banking and financial institutions are widely adopting predictive analytics in order to develop future working infrastructure. The key market players, on the other hand, are investing in new plans and projects. The companies are focusing on bringing novel and innovative predictive analytics solutions. In addition, several market players are hitting up the field with novel solutions and launches. Here are some of the latest happenings in the market.

Recently, Goldman Sachs, an American multinational investment bank and financial services company, has introduced an online banking service Transaction Banking, for huge and more streamlined businesses. The service offers features including virtual accounts, quick account opening, and predictive analytics to assist consumers predict future cash flows. Moreover, the solution aims to meet the demand of huge banking businesses to gain a good customer base.

Other than this, the Indian Institute of Technology (IIT) Roorkee, one of the leading technical institutions in Asia, has publicized the launch of AI in Banking Programme in association with WileyNXT. The novel AI in banking online programme aims to offer assistance to both the existing and graduated technology and finance professionals, to build a career in AI and analytics in the banking province. Furthermore, the programme would be for four-months and would focus on training IT professionals and graduates to apply Artificial Intelligence in the banking sector. Moreover, the course will offer a combined certification from IIT-Roorkee and WileyNXT.

The introduction of the Internet of things (IoT) based devices has boosted the demand for predictive analytics. On the other hand, the major issue for banking sectors such as increase in fraudulent activities including money laundering, payment card fraud, and accounting fraud have increased the adoption of predictive analytics in banking. According to a report published by Allied Market Research, the global predictive analytics in banking market is anticipated to reach $5.43 billion by 2026.

The assistance offered by predictive analytics to the bank and financial institutions for predicting their incoming and outgoing property payment and customer flow is the major factor that drives the growth of the market globally. Furthermore, the increase in demand from developing countries and the assimilation of AI in mobile banking applications usher a number of opportunities for the market players. Moreover, with the advancements and latest solution launches by the market players, the market is anticipated to grow exponentially

 
Alex Hillsberg

Alex Hillsberg

Chief editor of review team at FinancesOnline

Chief editor of review team at FinancesOnline Alex Hillsberg is an expert in the area of B2B and SaaS related products. He has worked for several B2B startups in the past and gathered a lot of first hand knowledge about the industry during that time.

 
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