Global Statistics

All countries
193,463,329
Confirmed
Updated on 23/07/2021 9:42 am
All countries
174,047,525
Recovered
Updated on 23/07/2021 9:42 am
All countries
4,152,562
Deaths
Updated on 23/07/2021 9:42 am

Global Statistics

All countries
193,463,329
Confirmed
Updated on 23/07/2021 9:42 am
All countries
174,047,525
Recovered
Updated on 23/07/2021 9:42 am
All countries
4,152,562
Deaths
Updated on 23/07/2021 9:42 am

AI in investing is about human empowerment, not displacement

Artificial intelligence (AL), a branch of computer science, enables machines to mimic intelligent human behavior to solve complex problems. It’s called artificial intelligence, since human intelligence is considered real intelligence. Machine learning (ML), a powerful subset of artificial intelligence, helps devices learn and improve from past experiences without the need for explicit programming. ML processes massive amounts of data to extract actionable insights that help create competitive advantage, and it has proven to be a game changer for the business world, driving aggressive growth, innovation and sustainability agendas across different verticals.

Given the fuzzy definitions of AI and ML in practice, it’s not easy to accurately measure their progression in quantifiable terms. However, several surveys and studies from time to time have given us a clear idea of ​​its speed.

in business and industry.

Machine learning and business intelligence
The growing volume and complexity of business data is driving the business adoption of ML in business analytics, which has progressed by leaps and bounds since the glory days of conventional extract, transform, and load (ETL) tools. ML has vastly improved business intelligence by processing and analyzing large and complex data sets to identify patterns that would otherwise go undetected.

Simply put, ML-enabled pattern recognition is a ‘machine way’ of identifying data regularity, which is about its stability, consistency, and symmetry, and classifying events based on input data. Therefore, more than simply monitoring behavior, analysis of user actions reveals actionable information about their behavior that is invariably complex and varied.

The importance of predictive machine learning can be measured by its success in different industries. Today, many retailers are creating personalized product recommendations based on purchasing patterns. Health insurance providers are developing information-rich consumer profiles. Digital media companies predict the success of entertainment shows to make smart streaming decisions. Food tech companies are customizing each customer’s landing page based on their granular food preferences, driving the shopper to a ‘buy’ based on the sheer joy of finding the recipe, ingredients, and cooking style. desired.

Machine learning applications in financial services
ML opportunities in the financial space. ML can help provide banking clients, as well as investors in the stock market, with curated pages that highlight offerings suited to their needs or with the sectors and stocks of their choice; More importantly, you can guide them toward better investment and banking decisions through prudent choices in light of prevailing market trends, realities, and customer needs.

It is pertinent to note the powerful dual use of ML in trading and investing. It can be used to analyze stocks, as well as to analyze the investment behavior of investors.

For inventory analysis, AI ensures a huge value addition: it collects clean data and processes and classifies to draw intelligent inferences through pattern recognition. In stock trading, it also helps minimize post-execution impact on stock prices by breaking orders into smaller chunks, as well as identifying arbitrage opportunities in various markets.

This snapshot of popular AI trading and trading tools gives a glimpse of a burgeoning market in the making.

Kavout Former Google executives Brainchild has developed “K Score” – an AI-enhanced stock ranking system that uses pattern recognition technology and a price forecasting engine.
Auquan Platform for asset managers to look for non-obvious connections, news, anticipation biases that affect investment decisions.
EquBot Artificial intelligence platform integrated with IBM Watson that enables faster data processing, AI-created portfolios, and sentiment analysis.
Black box actions It comes with a pre-trade scanner to detect most active stocks and their degree of volatility.
Neuronic Now part of Trading Technologies, it enables continuous assessment of compliance risk associated with complex business behaviors.
Sigmoidal Discover actionable patterns between capital market values ​​and expectations.

For the analysis of investor behavior, AI goes beyond mere personalization to forecast how such behavior will influence business decisions. This rotation brings to light invaluable information such as actionable segmentation of customers into different groups (and therefore targeting different product offerings) based on their spending and savings patterns.

Today, many technology companies are studying tons of unstructured data sets and extracting invaluable information and patterns to assess the reliability of company guidance disclosures, the correlation between projections and performance, and the likelihood of increases. and growth downturns. Additionally, digital assistant vendors are enabling guided conversations that simulate the “why” and “how” questions that an expert financial advisor is adept at asking and answering.

AI and ML in India … and the road ahead
According to a 2020 study by Analytics India and AnalytixLabs, 16% of analytics revenue across all companies is attributed to advanced analytics, predictive modeling, and data science. Although this participation is impressive, the fact is that the AI ​​market in India is still in a nascent stage.

Without a doubt, there is enormous scope for the use of AI in the future, beyond Behavioral Analytics, Robo Advisory, Stock Scoring, and Portfolio Diagnostics. Potential areas include both stakeholder-specific (such as customer onboarding, self-service offerings, vendor management) and system-specific (such as risk management, anti-money laundering, fraud detection).

As more and more users join the AI ​​train, data will only grow in volume, speed, accuracy, variety, and value. The graphic below (courtesy: German market data platform Statista) gives a clear idea of ​​the big data revolution that is brewing, which will soar higher on the wings of artificial intelligence and machine learning.

Yes Sec-Big data table

AI and ML in Investing: About Human Empowerment, Not Displacement

The capital market is enormously complex. Since market data, feeds are the main inputs of an algorithm; machines can miss dozens of elusive opportunities that only the human brain is adept at detecting. AI has the serious limitation of not offering a long-term strategy based solely on the status quo or past information. Humans can improve the probabilistic results of AI, validating them in light of intuition and discretion, such as the way a doctor studies an ultrasound image.

Artificial intelligence and machine learning are key enablers to improve investment and business decisions. Competent financial advisers make smart use of actionable AI and ML information that provides comprehensive analysis of stocks / sectors and investor behavior to create long-term wealth for different clients. This ultimate goal is achieved only through disciplined and diversified investments, in line with the respective income profiles, risk appetites, available market opportunities, and applicable incentives such as tax deductions and exemptions.

(
This article is written by Gopinath Natarajan, Head of Investments and Products, Yes Securities. The views are yours)

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