This post was originally posted on https://marutitech.com/
As global technology has evolved over the years, we have moved from television to the internet, and today we are smoothly and gradually adapting Artificial Intelligence. The term AI was first coined by John McCarthy in 1956. It involves a lot of the main things ranging from process automation of robotics to the actual process of robotics. It has become highly popular among large enterprises today owing to the amount of data these companies are dealing in. Increase in the demand for understanding the data patterns has led to the growth in demand of AI. AI processes are much more efficient in identifying data patterns than humans which is beneficial for companies to understand their target audience and gain insight. Thousands of companies all around the world are looking at AI as the next big thing for the finance industry.
AI basically is of two types –
Weak AI
Strong AI
Weak AI
Weak AI, which can also be described as Narrow AI is the system which is set up only to fulfill or accomplish a particular task. It is designed in a way which can help solve specific problems. Weak AI works according to the rules that are set and is bound by it. It does not go beyond the rules set. It focuses on only the narrow tasks and does the best job at it. Weak AI just like humans has the capability of all cognitive functions and is not distinct from the human mind. Though it cannot be defined as general intelligence, rather it is designed to act intelligently towards completing the narrow tasks that are assigned to it.
The best example of weak AI is Apple’s Siri which is governed by the substantial database of the internet. It appears very intelligent, primarily because of the conversations that it is able to hold with humans. Siri also is known for its witty remarks, but in actuality, it operates in a predefined manner. The “narrowness” is witnessed when the system engages in conversations that it is not designed to respond correctly to.
Same is the case of robots which are used in manufacturing companies. They respond very aptly when asked questions by the customers and clients. The responses are accurate and sometimes very witty too. The AI is capable of managing situations which are extremely complex in nature. But their intelligence level is restricted to providing solutions to problems that the system is programmed for, anything beyond that cannot be accomplished by it.
Strong AI
Strong AI, also known as full AI has much bigger prospects than the Weak AI. It is the artificial intelligence that has huge capabilities and functionality. It can broadly mimic the human brain. It is so powerful that the actions performed by the system are exactly similar to the actions and decisions of a human being. It also has the understanding power and consciousness.
Strong AI is actually a process which can be entirely equated to the human mind. Artificial Intelligence in every sense functions like the human mind with the extraordinary capability to understand everything that can be understood by it. The beliefs, cognitive states and perception which can be only found in humans are programmed in strong AI.
However, the difficulty lies in defining intelligence accurately. It is almost impossible or highly difficult to determine success or set boundaries to intelligence as far as strong AI is concerned. Hence weak AI is more preferable, primarily because of its ability to accomplish mainly assigned tasks with optimum efficiency. Weak AI does not fully encompass intelligence rather it focuses on completing a particular task it is assigned to complete. Hence it can be broken down into smaller processes.
Artificial Intelligence is the intelligence which is shown by machines and not humans. Devices using their cognitive functions identify and solve problems just like humans or in most cases solve better. It has successfully managed to create a significant impact by doing what is thought as impossible.
However, it is the finance industry which is claimed to have benefitted the most with the help of Artificial Intelligence. Cognitive computing, Chatbots, Personal Assistant, Machine Learning are all peripherals of AI used in the finance industry extensively nowadays. Some financial organizations have been investing significantly in AI for years now, and much many are now willing to invest in AI.
AI is predicted to replace humans in the near future as companies start looking for features such as machine learning, personal assistants/advisors or digital labor. Owing to the big data, cloud services, and hyper processing systems, AI has gained popularity. But the greatest challenges faced are lack of trust, biases and majorly regulatory concern. Hence, companies today prefer a reliable option in the form of Augmented Intelligence which is designed to assist humans.
Artificial Intelligence can be used abundantly in processes which involve auditing of financial transactions. Also when it comes to analyzing an enormous number of pages of the tax changes, AI can be of great help. It can be expected in the near future to see companies relying on AI to make significant firm related decisions. AI also has the capability to identify how customers are going to react to various situations and problems. Artificial Intelligence is going to help people and firms make smarter decisions at a very quick pace. But the key here is to find the right balance between humans and machines.
5 WAYS HOW AI HAS TRANSFORMED THE FINANCE INDUSTRY ARE –
1. Risk Assessment:
Since the very basis of AI is learning from past data; it is natural that AI should succeed in the Financial Services domain, where bookkeeping and records are second nature to the business. Let’s take the example of credit cards. Today, we use credit score as a means of deciding who is eligible for a credit card and who isn’t. However, grouping people into ‘haves’ and ‘have-nots’ is not always efficient for business. Instead, data about each individual’s loan repayment habits, the number of loans currently active, the number of existing credit cards, etc. can be used to customize the interest rate on a card such that it makes more sense to the financial institution that is offering the card. Now, take a minute to think about which system has the capability to go through thousands of personal financial records to come up with a solution- a learned machine of course! This is where AI comes in. Since it is data driven and data dependent, scanning through these records also gives AI the ability to make a recommendation of loan and credit offerings which make historical sense.
AI and ML are taking the place of a human analyst very fast as inaccuracies which are involved in human selection may cost millions. AI is built upon machine learning which learns over time, less possibility of mistake and analyzing vast volumes of data; AI has established automation to the areas which require, intelligent analytical and clear-thinking. Financial Services Chatbots have indeed proven themselves as a powerful tool to customer satisfaction and an unmatched resource for the enterprises helping them save a lot of time and money. Now, getting back to Facebook’s endeavors in designing and developing Bots to make negotiations the way humans do, let us analyze the chances of the success of this research. This new technology will not only change the way we do business but also non-commercial activities. The example of non-commercial activities can include fixing meeting time. The Bots can fix up the meetings keeping in mind the availability of everyone involved in the meeting.
2. Fraud Detection And Management:
Every business aims to reduce the risk conditions that surround it. This is even true for a financial institution. The loan a bank gives you is basically someone else’s money, which is why you also get paid an interest on deposits and dividends on investments. This is also why banks and financial institutions take fraud very, very seriously. AI is on top when it comes to security and fraud identification. It can use past spending behaviors on different transaction instruments to point out odd behavior, such as using a card from another country just a few hours after it has been used elsewhere, or an attempt to withdraw a sum of money that is unusual for the account in question.Another excellent feature of fraud detection using AI is that the system has no qualms about learning. If it raises a red flag for a regular transaction and a human being corrects that, the system can learn from the experience and make even more sophisticated decisions about what can be considered fraud and what cannot.
3. Financial Advisory Services:
According to the Pwc Report, we can look forward to more robo-advisors. As the pressure increases on financial institutions to reduce their rates of commission on individual investments, machines may do what humans don’t- work for a single down payment. Another evolving field is bionic advisory, which combines machine calculations and human insight to provide options that are much more efficient than what their individual components provide.Collaboration is key. It is not enough to look at a machine as an accessory, or on the other end, as an insufferable know-it-all. An excellent balance and the ability to look at AI as a component in decision-making that is as important as the human viewpoint is the future of financial decision-making.
4. Trading:
Investment companies have been relying on computers and data scientists to determine future patterns in the market. As a domain, trading and investments depend on the ability to predict the future accurately. Machines are great at this because they can crunch a huge amount of data in a short while. Machines can also be taught to observe patterns in past data and predict how these patterns might repeat in the future. While anomalies such as the 2008 financial crisis do exist in data, a machine can be taught to study the data to find ‘triggers’ for these anomalies, and plan for them in future forecasting as well.What’s more, depending on individual risk appetite, AI can suggest portfolio solutions to meet each person’s demand. So a person with a high-risk appetite can count on AI for decisions on when to buy, hold and sell stock. One with a lower risk appetite can receive alerts for when the market is expected to fall, and can thus make a decision about whether to stay invested in the market or to move out.
5. Managing Finance:
Managing finances in this well-connected and the materialistic world can be a challenging task for so many of us, as we look further into the future we can see AI helping us to manage our finances. PFM (personal financial management) is one of the recent developments on the AI-based wallet. Walletstarted by a San Francisco based startup, uses AI to builds algorithms to help the consumers make smart decisions about their money when they are spending it. The idea behind the wallet is very simple it just accumulates all the data from your web footprint and creates your spending graph. Advocates of privacy breaching on the internet may find it offensive but, maybe be this is what lies in future. Thus it has to be the preferred personal financial management in order to save time from making lengthy spreadsheets or writing on a piece of paper. From a small-scale investment to a large scale investment AI commits to be a watchdog of future for managing finances.
Without a speck of doubt, AI is the future for the finance industry. Since the speed at which it is making progressive steps towards making the financial processes easier for the customers, it is very soon going to replace humans and provide faster and much more efficient solutions. Bots are gradually evolving as innovations are being in the AI sector. Massive investments are being made by the firms who are seeing this as a long-term cost-cutting investment. It helps the companies in saving money of hiring humans and also avoiding human errors in this process.
Though it is still in its nascent stage the speed at which it is progressing to evolve the finance sector, it can be well expected that the prospects are going to lead to minor losses, smarter trading and of course top-notch customer experience.