Contextual intelligence and data analytics in machine learning can be a mouthful for finance teams. In fintech, we are sitting on a gold mine of big data, thanks to billions of data points generated from online banking transactions, emails, mobile apps, location data, and credit card information.
All these are gleaned to meet unique demands in fintech, resulting in many fintech-specific opportunities and risks.
In the recent times, more and more financial institutions and machine learning course providers have come together to create a data-driven ecosystem that benefits not just the companies and customers that they serve, but also the employees who work in fintech enterprise projects.
In this article, we point out to the major factors that make deep learning and machine learning course central pieces for finance teams in the modern context of doing business.
How does Deeper Machine Learning Happen?
Machine Learning and Artificial Intelligence (AI) are two powerful allies in the financial ecosystem. With these capabilities, finance teams can unravel the dark side of data and security issues that often cause the industry loss in billions of dollars.
When we talk of loss, let’s get the numbers and intent right!
According to an independent research on the fintech industry, an intelligence firm reported that identity fraud is the biggest scam that the companies complain about.
We know how machine learning is already used to target crime-prone areas in the operations. But, the results are yet to meet the expectations.
Finance teams are using AI and ML capabilities to make banking and credit unions more agile and innovative with their abilities to secure physical and digital payments systems.
There is an obvious drive to move toward the micro-economic dimensions of digital banking, with more and more players in POS and e-wallets taking shape in fintech industry.
However, the innovations have met with startling hurdles — largely due to the inability of various finance operations in fraud related losses and account takeovers by hackers.
According to the Global Identity and Fraud Report, nearly 60% of the finance teams with matured fintech stacks have acknowledged targeting by fraudsters and credit scams. Most of these are related to the hacking of identity, digital payments takeovers, credit card frauds, and account opening data leakage.
Leading finance-specific companies use matured deep learning and computer vision technologies to create secure accounting and payments route.
Not only finance teams but also investment firms and VCs benefit from integrating Machine Learning stacks with their back office and middle management services and operations to move their financial assets securely.
Why Finance Teams Have a Slow Growth Rate with ML Techniques?
Fintech is still very nascent in its maturity growth plan. It is moving at less than 5% growth annually, and the large chunk of revenue lies in the closed economic systems such as those in China and Russia. Despite economic proximity between the US, Canada and India, the fintech market has failed to capitalize onto the 2 billion+ consumer base that North America and APAC regions have to offer.
We can point to seven critical factors that are stopping finance teams from fully leveraging the power of AI and ML in their operations. These are:
- Lack of Universal Data Governance Laws and Universal Customer Data Systems to track financial assets
- Lack of Political will to segregate and prosecute financial criminals
- Poor economic status that leads to slower adoption of technologies to track these crimes
- Costly technology
- Lack of Research and Development
- Growing gaps between demand and supply of fintech workforce that are skilled in AI and ML projects
- Disparity of payment and salary in these projects, despite investment firms taking wide-scale interest.
Rise of Indian Fintech Talent
It is not a hidden fact that Indian technology market is buzzing with excitement. Out of 10 hired professionals, 4 are Indians who are directly involved in leading projects for card manufacturers, data management, Cloud and IT architecture, mobile marketing, Mobile Wallets, and third party personalization and branding campaigns.
Large fintech groups led by Paytm, Apple Pay, Amazon, Uber, Google Pay and Samsung Pay, in addition to the traditional card technology and payments company such as VISA and Mastercard have grown outward with their secured digital identities in diverse domestic and international client base.
With more and more Indian students and professionals joining machine learning course to enhance their business intelligence, computing and cloud security skills, we can expect the fintech market to expand exponentially in the next five to six years.