Here’s a quick video on what Fintech is!
#FinTech #fintech #finacial #technology #fintechclarified

This is a talk I did recently at Microsoft about FinTech, RegTech, AI, machine learning and data in banking.

1:10 Introduction to FinTech
3:57 Democratising AI
6:15 AI == Innovation
8:03 Compute + Data Explosion
8:47 Microsoft AI stack
11:33 New ML Stack (Late 2017)
13:05 Azure AI Supercomputer
13:45 Journey to AI Automation
15:18 Front office banking ML use cases
16:08 Back office use ML cases
17:08 Partner spotlight: AYASDI
18:47 Our Vision
19:21 Take home messages and QnA

COMPLIANCE STATEMENT:
* THIS IS NOT A MICROSOFT YOUTUBE CHANNEL
* WE ARE NOT A SPOKESPEOPLE FOR MICROSOFT I.E. MY OPINIONS
* THERE IS NO MICROSOFT-CONFIDENTIAL INFO IN THIS VIDEO
* I WORKED FOR MICROSOFT AT THE TIME THIS CONTENT WAS CREATED

We are talking about Artificial Intelligence applications in finance. Let’s review the current list of trends and what to expect from the industry.
▶ Contact Jelvix: [email protected] | jelvix.com

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▶ USEFUL LINKS:
– Machine Learning vs. Deep Learning vs. Artificial Intelligence – https://jelvix.com/blog/ai-vs-machine-learning-vs-deep-learning
– Application of AI in Finance – https://jelvix.com/blog/ai-in-finance

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▶ About this video:
How is AI applied in finance?
1. Automation
It enables organizations to boost productivity and cut operational costs. Statistics say that AI helps companies save up to 70% of the costs associated with repetitive tasks.

2. Credit Decisions
We once developed AI-based software that helps banks assess potential borrowers. It immediately analyzes countless factors thus saving costs and making the process much faster. Sounds exciting, doesn’t it?

3. Trading
AI-driven trading systems can analyze massive amounts of data much quicker. You won’t believe it, but predictions made by AI algorithms are more accurate because they can analyze a lot of historical data.

4. Risk Management
AI can handle risk management tasks much more efficiently and analyze various financial activities in real-time.

5. Fraud Prevention
AI-driven fraud detection tools can analyze clients’ behavior, track their locations, and determine their purchasing habits. Therefore, they can quickly detect any unusual activities.

6. Personalized Banking
AI-powered chatbots minimize the workload of the call centers.
There are also many apps that offer personalized financial advice so that users can achieve their financial goals, track regular expenses, income, and purchasing habits.

Financial companies rely on artificial intelligence to improve everything related to regulatory compliance, lending decision-making, fraud detection, credit risk assessment, wealth management, and beyond. This session covers the current work in Regtech, Risktech, and ESG as well as broader applications of AI in the Fintech space at FIS Global.