AI covers an expansive range of solutions and covers major aspects ranging from robotics based process automation to the core robotics process. Big companies are more interested in AI technology as they are aware of the fact that Artificial Intelligence can handle huge amounts of business data.
There are two principal types of Artificial Intelligence now. They are respectively as,
Narrow AI which is also called weak AI is a system for accomplishing a particular task. Experts basically created this AI to solve specific problems. Narrow AI works on the basis of a preset rules. Narrow AI focuses on tasks and carries them out based on certain conditions and rules. Weak AI is much similar to our ability to make decisions with cognitive functions for solving particular problems.
The Siri voice assistant of Apple is a great example of Narrow AI. It carried out tasks in a predefined way and based upon certain present rules. We perceive it as intelligent thanks to its wit and prompt answers but actually it performs based on a robust internet database. The robots in the manufacturing sector use the same Narrow AI technology. Based on preset conditions and rules they respond intelligently to the questions asked by the customers and clients. At certain times, these responses appear as very accurate and intuitive.
Full AI which is also called strong AI comes with numerous capabilities and functionalities as compared to narrow AI. In simple terms, strong AI can understand and perform tasks much like a real human. It is so powerful and inclusive in considerations that the tasks performed by such a system can appear as carried out by real humans. This type of AI has a broader and detailed understanding comparable to humans.
Full AI to a great extent works much like the human mind in many ways using the rich capabilities to understand almost everything. At the same time, it is just impossible to set limits to intelligent capabilities when it concerns strong AI.
It looks quite normal that the technology will replace many humans as more and more financial companies are taking interest in AI-based capabilities and features such as digital labor, machine learning, personal advisors, etc. This has got a bigger boost owing to cloud services, big data analytics, and powerful processing systems. All these factors are helping AI to gain serious attention. In spite of this fact, there are many challenges corresponding to trust, biases, and some regulatory concerns. This is making companies play defensive and play on the safer side by embracing AI only at a slower pace to assist Human Resources.
The impact of AI on the financial industry has been multifarious. From cutting cost on human resources to boosting profit to ensuring more efficiency and professionalism, the impacts are too many. AI-based back-office operations have already become a reality. AI integration with external services and apps are also boosting the pace of this transformation. Let's have a quick look at the key impacts of AI on the financial industry.
Boosting customer loyalty: Increasingly, the past methods of differentiation between financial companies are eroding and so AI is offering an opportunity for banking and financial institutions to take the competition to customer loyalty.
Self-servicing finance: Future customers of the financial sector experience banking as more intelligent thanks to AI. The AI implementation helps automating the customers financial transactions and boosts efficiency.
Collective solutions for common problems: Collaborative or collective solutions based on shared datasets can greatly improve the accuracy, timeliness, and efficiency of non-competitive financial functions. This will create mutual efficiencies in banking operations and boost safety.
Decentralisation of market structure: AI can make search and product comparison easier for customers. This will help pushing the firm structures to market extremes, amplifying the returns for big players while creating new opportunities for innovative players.
Diverse data alliances: Thanks to the AI ecosystem, financial institutions can capitalise on the diversity of data and can manage partnerships with various players with less operational risks.
Empowering data regulators: Industry regulations ruling the privacy and portability of data will get a boost through AI. AI based data governance will boost the relative ability of financial and non-financial institutions to take care of anomalies and security threats more proactively.
A balanced talent acquisition: Talent acquisition will be the most prominent area of benefits from the implementations of AI in financial institutions. Transitioning talent sourcing as per technology need will take center stage.
Few ethical dilemmas: AI will facilitate a collective evaluation of principles and supervisory techniques to address some ethical gray areas and uncertainties such as the concerns over AI taking away jobs of humans.
Understanding of the successful AI strategies in the financial sector must be substantiated with real world instances. This also needs identification of core institutional and larger challenges and uncertainties common to the industry.
There are many ways AI is helping the financial industry. Here are 3 principal ways AI is helping the financial and banking industry.
AI by taking references for past data in the financial industry, makes risk assessment easier. In the financial industry where bookkeeping and records are integrated elements of the business, the data driven assessment approach of AI is highly effective.
For example, credit card and loan giving companies use a credit score to evaluate eligible and non-eligible individuals. They can use AI based data analysis to get details of the loan repayment habits, number of in-use credit cards, active loans, etc. An AI based system digging into thousands of personal financial records can deliver results optimised for machine learning.
In many ways, AI and ML are providing better and efficient alternatives to human analysts with a speed advantage. Machine learning-based AI assessment performs more accurately and leaves no chances of errors while analyzing large volumes of data. AI by automating all areas needing smart analysis makes assessment and evaluation easier for financial institutions.
AI makes it to the top of the list of preferred technologies when it comes to fraud control and security enhancement. AI technology by leveraging the customer history data like transaction behaviors for account-related queries, can detect anomalies and potential risks.
Finally, AI can help investment companies who depend a lot on data scientists and computers for understanding future market patterns. Since the trading and investment industry depends heavily on predictability, AI based algorithms can deliver great market insights based on large volumes of data.
AI is continuing to transform the financial industry. In the years to come, we can expect a lot of innovations driven by AI to push for growth in the sector.
About the Author:
Chirag Mudsa is the CEO of leading android app development company, CMARIX TechnoLabs Pvt. Ltd. He is a goal-driven tech evangelist for a long time of 17 years, specializing in web and mobile development domains. His innovative spirit, strong leadership skills and a profound commitment to organizational growth have given him a key leading position in the industry.
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