Скачать или смотреть Top 5 Applications of Python and AI in Finance

In this video, I am going to cover top 5 applications of Python and AI/ML in Finance.

1. ROBO ADVISORS
Artificial intelligence and machine learning have become powerful in providing investment advice to customers.

Robo advisors are financial advisors that do not entail human intervention and are based on algorithms.

Robo advisors work by consuming client current financial status, target return, risk appetite and financial/life goals and automatically provide accurate financial advice such as portfolio allocation (% bonds, stocks, real estate, and ETFs).

Betterment and Wealthfront are two of the top investment companies that rely on Robo advisor to provide financial advice to clients

2. ROBOTIC PROCESS AUTOMATION

Python and machine learning are being widely adopted in finance to automate various processes to reduce time and cost.

Process automation could be in a form of paperwork automation, chat bots, and customer service automation.

Natural language processing techniques are being leveraged to extract useful information from documents, perform topic modeling, understand customer’s sentiment from tweets and news data, respond to customer’s queries and process legal documents.

3. FRAUD DETECTION & SECURITY
Machine learning could be used to automatically detect fraudulent transactions, detect suspicious transactions on customers account, and stop the transactions until further information is acquired.

Machine learning classifier models are being trained on millions of fraudulent and non-fraudulent transactions with a goal to ensure prediction accuracy and reduce false positive/negative predictions.

4. HIGH FREQUENCY TRADING
AI and machine learning are used to analyze historical stock prices data, extract news sentiment, current market trends and predict future price movements of securities.

Algorithmic trading models are being trained to execute buy/sell thousands of securities at a fraction of a second which is known as high frequency trading.

5. INSURANCE AND LOANS UNDERWRITING
Insurance companies and banks rely on machine learning to assess clients risk and decides whether a client qualifies for a loan or insurance policy based on his/her features such as historical data, credit score, income, and debt amount.

These algorithms are being trained with millions of data points to automatically perform creditworthiness assessment with minimum to no human intervention.


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