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Application and Impact Of Artificial Intelligence in Financial Sector:

“We’re entering a new world in which data may be more important than software.”

Really???

Artificial Intelligence in finance may be the one technology that’s paying dividends. Artificial intelligence has given the world of banking and the financial industry as a whole a way to meet the demands of customers who want smarter, more convenient, safer ways to access, spend, save and invest their money.

Some applications, including risk management, alpha generation and stewardship in asset management, chat bots and virtual assistants, underwriting, relationship manager augmentation, fraud detection, and algorithmic trading. In insurance, we look at core support practices and customer-facing activities. We also address the use of AI in hiring.



Many banks, start-ups, traditional business using AI in the financial services sector as fraud detection, advisory services, personal financial management and trading assistance and execution. The rise of AI in the financial industry proves how quickly it’s changing the business landscape even in traditionally conservative areas.

More often than not, we don’t realize how much Artificial Intelligence is involved in our day-to-day life.

Mining Big Data

Data mining is the process of sorting through large data sets to identify patterns and establish relationships to solve problems through data analysis. When doing data mining, it is also important to properly showcase the analysis results. In layman terms Data mining is also known as discovery of knowledge in data. It uses sophisticated mathematical algorithms to classify, divide, segment the entire data, pre-process it as necessary and evaluate the possibility of future events.

Mining Big data is the main and most important thing is comparison to all sectors because if we don’t have abundant of data and if it is not properly mined our hands are tight to do any activity with data. Financial sector have been collecting far more information than this for quite some time, but only a recent push for data monetization has forced developers to take a second look at countless other factors. AI-based solutions determine the value of both physical and investment products by looking at the following:

-How demand for one type of product influences another

-Price fluctuations of different investment products against one another. This can, in return, enhance the benefits of inventory management.

-The geographical location of consumers who make financial choices

-Post-trade allocation preferences of different investors

-Trading patterns that shape hourly prices

-Volatility of prices traded on an open exchange

-Relative costs of goods and services in different markets

Here are just some of the most popular examples of AI in finance.

Trading with Artificial Intelligence

Data driven investment have been rising and can help manage and augment rules and trading decisions, helping process the data and creating the algorithms managing trading rules. It’s also called algorithmic, quantitative or high-frequency trading.

It has been expanding rapidly across the world’s stock markets, and for good reason: artificial intelligence offers multiple significant benefits. Investment firms have implemented trading algorithms based on sentiment and insights from social media and other public data sources for years.

Here are several types of operations exercised by AI for trading companies and their customers:

-Developing exchange algorithms based on technical analysis.

-Searching for new big data processing methods to use them in traditional analysis.

-Combining machine learning with financial examination to design robotized decision-making advisory.

-Developing investment strategies for hedge funds.

-“Unmanned” end-to-end investment fund management.

-Analyzing big data captured on social media and thematic websites including customer sentiment and reviews for further forecasting.

-Compiling analyst rating and processing their forecast for further selection of the best trading strategies.

-Developing economical models during high volatility and upon market disruptions.

-Detecting market conspiracy and manipulation.

These Artificial Intelligence Systems can trade the market better than any human being can.

Investing with Artificial Intelligence

Artificial Intelligence advancing rapidly in virtually every industry .AI-based processes and increasing profitability with techniques that will scale. Some firms are using AI to improve the way they analyze securities and make investment decisions, while others use it to improve core operational processes. AI can lead to better predictions, fewer errors, and greater efficiency for the investment industry.

Many of the investment firms in the spotlight for using AI are quantitative hedge funds and asset managers. But a growing number of companies, large and small, are finding new ways to incorporate the technology into their own operating models. Firms across the buy-side and sell-side are now using AI to execute trades, manage portfolios, and service their clients.


Not all companies will adopt AI at the same pace: some will be followers in an effort to limit spending and avoid short-term uncertainty. But doing nothing is not a viable option to remain competitive over the long-term.

We believe that to gain the benefits AI has to offer, your firm should concentrate on four key areas:

-Creating a broad AI strategy

-Focusing on your people

-Improving your processes

-Making sure that your AI systems are well protected

THE FUTURE OF INVESTMENT IN AI COULD BE DECIDED BY AI ITSELF

Banking with Artificial Intelligence

Banks deal with a lot of data day in and day out — client data, demographic data such as name, email address, home address, age, income, etc. and then financial data — investments, transactions, mortgages, credit cards, etc.

To better serve their customers as one integrated bank, banks want to aggregate this data in one place and derive insights out of it to create contextualized hyper-personalized strategies for their clients.


Using AI reduces the time spent in analyzing client data, finding patterns out of it and AI makes it possible to generate near-accurate insights about a particular cohort of clients or a client itself.

While online banking and technology-driven disruption have brought about improvements in accessibility and customer service, hacking and cybercrime have become common problems. Combating these issues requires enormous amounts of resources — and incurs costs that are inevitably passed on to consumers.

So reducing time and cost is definitely one success metric in using AI in banks. But also, how can banks offer the maximum value to their individual clients, rather than generic advice/recommendations on products.

There are other applications of just automating processes and forecasting/predicting etc often include:

-Predicting the price of investments/stocks

-Fraud detection in credit card activity

-Forecasting the home value in a particular neighborhood

-Sending you the best or cheaper offers according to your spending patterns

The customer’s interaction with the product or the service does not change, however now after using AI you might find efficiencies here and there. You as a customer might feel the product or platform is speaking to you, that things are getting done a bit faster, my queries are getting addressed sooner, I’m able to provide feedback to the platform and it’s actually improving. Many banks are still operating in a more traditional way. However, as a result of the increased adoption of AI technologies, it is now possible for them to join the digital world and remain competitive with some of the biggest digital players in the market.

Credit decision with Artificial Intelligence

Artificial Intelligence provides a faster, more accurate assessment of a potential borrower, at less cost, and accounts for a wider variety of factors, which leads to a better-informed, data-backed decision. Credit scoring provided by AI is based on more complex and sophisticated rules compared to those used in traditional credit scoring systems. It helps lenders distinguish between high default risk applicants and those who are credit-worthy but lack an extensive credit history.



Objectivity is another benefit of the AI-powered mechanism. Unlike a human being, a machine is not likely to be biased.

Digital banks and loan-issuing apps use machine learning algorithms to use alternative data (e.g., smartphone data) to evaluate loan eligibility and provide personalized options.

Risk Management with Artificial Intelligence

Artificial Intelligence is a game-changer for risk management in finance as it provides banks and credit unions with tools and AI solutions to identify potential risks and fraud. Enormous processing power allows vast amounts of data to be handled in a short time, and cognitive computing helps to manage both structured and unstructured data, a task that would take far too much time for a human to do. Algorithms analyse the history of risk cases and identify early signs of potential future issues.

Artificial intelligence in finance is a powerful ally when it comes to analyzing real-time activities in any given market or environment; the accurate predictions and detailed forecasts it provides are based on multiple variables and vital to business planning.

Artificial intelligence and risk management perfectly align when there is a need for handling and evaluating unstructured data. It is estimated that risk managers of financial institutions will focus on analytics and stopping losses in a proactive manner based on AI findings, rather than spending time in managing the risks inherent in the operational processes.

AI solutions are able to fuel financial institutions with trusted and timely data for building competence around their customer intelligence and successful implementation of their strategies.

Fraud Prevention

For a number of years now, artificial intelligence has been very successful in battling financial fraud — and the future is looking brighter every year, as machine learning is catching up with the criminals.

AI is especially effective at preventing credit card fraud, which has been growing exponentially in recent years due to the increase of e-commerce and online transactions. Fraud detection systems analyze clients’ behaviour, location, and buying habits and trigger a security mechanism when something seems out of order and contradicts the established spending pattern.

Banks also employ artificial intelligence to reveal and prevent another infamous type of financial crime: money laundering. Machines recognize suspicious activity and help to cut the costs of investigating the alleged money-laundering schemes. Aggregators like Plaid (which works with financial giants like CITI, Goldman Sachs and American Express) take pride in their fraud-detection capabilities. Its complex algorithms can analyze interactions under different conditions and variables and build multiple unique patterns that are updated in real time. Plaid works as a widget that connects a bank with the client’s app to ensure secure financial transactions.


Robotic Process Automation In Banking

Artificial Intelligence (AI) & Robotic Process Automation Implication is a hot topic across various industries. Driven by its best capabilities, today, it becomes like a precious solution for all businesses to drive their performance in this digital world.

Intelligent character recognition makes it possible to automate a variety of mundane, time-consuming tasks that used to take thousands of work hours and inflate payrolls. Artificial intelligence-enabled software verifies data and generates reports according to the given parameters, reviews documents, and extracts information from forms (applications, agreements, etc.).



In particular, the global largest banks are doubling their investments in these emerging artificial intelligence technologies. The primary reason behind this increased adoption is to avoid fraud transactions and secure the entire banking network using ethical AI systems. Employing robotic process automation for high-frequency repetitive tasks eliminates the room for human error and allows a financial institution to refocus workforce efforts on processes that require human involvement.

Even though banks deploy extremely secured fraud detection methods, the banking systems highly prone to attacks. The attackers are retrieving customer’s identity data by breaking the IP addresses of cloud-servers.

This can be avoidable by using AI, machine learning, and deep learning techniques. Using pattern recognition and clustering algorithms, AI-enabled systems can efficiently discover and counteract fraud transactions, illegal access to the user account, and information systems.

Conclusion

What to Expect in The Future from AI in the Financial Industry?

Predictions for the soon-to-come AI applications in financial services is a hot topic these days but one thing is for sure: AI is rapidly reshaping the business landscape of the financial industry.

There are high hopes for increased transactional and account security, especially as the adoption of block chains and cryptocurrency expands. In turn, this might drastically reduce or eliminate transaction fees due to the lack of an intermediary.

All kinds of digital assistants and apps will continue to perfect themselves thanks to cognitive computing. This will make managing personal finances exponentially easier, since the smart machines will be able to plan and execute short- and long-term tasks, from paying bills to preparing tax filings.



As we can see, the benefits of AI in financial services are multiple and hard to ignore still err on the side of caution, fearing the time and expense such an undertaking will require –, and there will be challenges to implementing AI in financial services and also AI is essential to gain insights into the current Financial Services ecosystem.

Recap

What I have presented here are the insights of Application and Impact Of Artificial Intelligence in Financial Sector. I hope you learned something today.

Always remember that solid business questions, clean and well-distributed data always beat fancy models.

Feel free to leave a message if you have any feedback, and share with anyone that might find this useful.

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