How AI is changing global financial sector towards better and precise data analysis and statistics
Finance industry has been implementing technological advancement especially artificial intelligence for speed, accuracy, and efficiency in the past decades. The term of AI of “Artificial Intelligence” was first coined by John McCarthy in 1956.
Let’s discover the basic types of AI.
Having the knowledge of how AI works in important industries, we will be able to evaluate the concerns that there are risks to be very dependable on robots to run the work on behalf of human and take adequate and counteractive measures to eliminate the risks.
Types of Artificial Intelligence
The root of AI is algorithms. Generally speaking, there are 2 types of Artificial Intelligence; Weak AI and Strong AI.
Weak AI, also known as Narrow AI is usually the program that is set up to fulfill simple particular tasks, designed in a way that which helps to solve specific problems.
Weak AI lacks of human consciousness, although it may be able to simulate it. Best example of Weak AI is Apple’s Siri which is based on the universal database of the Internet. This kind of AI is limited of their intelligence, restricted to only what they are programmed for such as solving problems.
On the other hand, Strong AI which sometimes goes by the name of Full AI, has broad capability and functionality which enables it to mimic human brain. This Full AI is able to understand power and consciousness, while taking actions that are largely similar to human’s decisions and behavior.
Strong AI also can be equated to a human mind: because of the beliefs, cognitive states and perception which once exclusively belongs to human, now can be programmed into an AI.
How AI is Shaping the Finance Industry
As the foundation of artificial intelligence is learning from the past data, the finance industry is the best place for AI to be used, as the data from bookkeeping, containing all the consumers’ spending and income is huge and massive.
Different uses of AI in financial industry. Credits: SlideShare
1. Risk Assessment and Management
Financial institution usually put risk assessment and management as one of their top priorities as it carries far-reaching consequences.
The huge database of consumer’s spending and income in finance industry would be a nuisance for humans to evaluate and analyse but artificial intelligence would be the best for producing best analysis results and assessment.
Let’s take an example, your credit score. Today we use credit score as a mean to decide either an individual is eligible for loan or credit card. However, to categorise your consumer into ‘who’s eligible’ and ‘who’s not eligible’ are not very profitable and efficient for business.
Instead, by using AI can assess the individual or institutional repayment habits, the loans one currently on, etc and from these data and analysis, the possibility of risk should the bank or financial organisation lend out a sum of money.
2. Fraud Detection
In many areas such as insurance, banking and retail are very prone to fraud. Machines are one of the best options to deal with and identify primary fraud score (which relates to fraudulent accounts) and a transactional score which identifies a specific fraudulent transaction.
Machine learning can identify accurately the fraud patterns or anomalies using predictive pattern analysis in EuroPay, Mastercard and Visa (EMV) driven online fraud, account takeover fraud, returns fraud and automated fraud among others.
3. Algorithmic Trading
Investment companies always found themselves to be relying on data scientists and help of technology to determine the recurring potential pattern in the ups and downs of the charts.
Technologies are usually useful to crunch the big numbers, and thanks to machine learning, now AI can also be counted on to understand the subtlety and intricate nature of some tasks such as for stocks trading.
As machines are capable of carrying out complex calculations in split seconds, it is clear to see why the growth in online trading has been accompanied by the development of automated trading systems and algorithmic trading.
As traders and brokerages seek to increase the profitability of their systems, they have adopted more sophisticated tools and customized their algorithms in order to maximize their earnings potential.
There’s a long list of benefits that algorithmic trading (or also known as algo-trading) can provide. Some of the benefits are that trades executed at the best possible prices, instant and accurate trade order placement (thereby high chances of execution at desired levels), trades timed correctly and instantly, to avoid significant price changes and reduced transaction costs.
AI Platform for the Mass Market
MyFinB, who is working with Morgan Capital, aims to fill the big gap and demand in today’s current professional space – providing finance and business professionals a fast and simplest way to analyze financial data.
With presence in over 25 countries, MyFinB strives to create an exceptional tool driven by proprietary natural language generation platform. It would transform basic financial data into meaningful and insightful information that any users can simply comprehend – and more importantly, make better decisions.
Technological improvements such as AI would be absolutely necessary to be implemented in the finance industry as the vast data is getting broader and more complex. The cost to train and hire human resources would not be viable as the cost could be high and there are endless possibilities of organic inaccuracy.
In question of the security threat that AI possibly pose, undoubtedly there are some raising security and humanity issues of machine consciousness.
Regardless, the rise of automation would push for a firm regulation and be imposed as a governance system that would ensure AI and its allied technologies do not cross boundaries. Technologists must build trustworthy automation technologies which will infuse confidence within our social, political and business environments.