The internet is not the same as it was a decade ago. A lot of changes and developments have taken place and taken the virtual world to the next level. People are far, yet they are virtually connected almost 24/7. For now, it can be said that the internet is the next big thing that people are talking about. With the augmentation of artificial intelligence and Machine Learning Solutions, the internet has become like just another world. Organizations are making use of this technology to grow their online business presence and attract more lucrative prospects to their business.
The modern-day industries follow a consumer-driven approach, where the customer experience is considered to be the epicenter of the business. With the continuous evolution of modern technology is driving consumer behavior, an important aspect of modern technology has become a transformation champion – Artificial Intelligence. Ranging from manufacturing agencies to digital services, the AI field has succeeded in to change the industries. In this digital era, AI is having a crucial position in reshaping the area of web development.
AI in the real-world
The AI technology which is showcased in the science fiction movies had enthralled the viewers and was representing the future world. This is now taking the shape of reality. Not just the industry up-gradation but modern lives have been significantly redefined through the use of AI.
The global market for AI is estimated at almost USD 7.35 billion in the year 2018. This is expected to rise by almost ten folds until the end of 2025.
The fields of Artificial Intelligence and Machine Learning are expected to spread across the consumer market in the coming years, and this would directly impact the performance of web development.
In the current comprehensive and competitive market scenario, many companies are using several tools for deploying AI technologies to reinvent themselves. The aim is also to have a single point of contact between the company and the consumer, websites even need to be upgraded on a priority basis. Hence, it becomes important for web developers as well as application developers to adapt themselves to the changing environment and adopt modern AI for their evolution. The AI advancements have even helped in the enhancement of computer performance and even increasing storage capabilities. All of this has made it possible for the companies to handle large quantities of data and to leverage the big data analytics for their development.
AI in the finance industry
Artificial intelligence usage in the finance sector is transforming how we interact and deal with money. It is helping the financial sector to streamline and to optimize the processes ranging right from credit decisions to quantitative trading as well as financial risk assessment and management. Below mentioned are some of the best examples of making use of AI for the financial function benefits.
Using AI for finance credit decisions
In finance, the Credit is the king. According to a recent study, it was found that 77% of the consumers used to prefer making payments with a debit or credit card as compared to only 12% of the people who favored using cash. But the easier payment options are not the only reason that the availability of credit is considered to be important for the consumers.
Having good credit helps in receiving financing options, landing in good jobs and renting a property, to name a few examples. Having many of life’s important necessities dependent on credit history, the approval process for the loans and cards is considered to be more important than ever now.
The Artificial intelligence solutions are assisting banks and credit lenders to make smarter underwriting decisions by making use of a variety of factors that can accurately assess the traditionally underserved borrowers, such as millennials, which are present in the credit decision-making process. Let’s have a look at some of these:
ZestFinance is the creator of the famous Zest Automated Machine Learning (ZAML) platform. It is an AI-powered underwriting solution that helps the companies to assess the borrowers with almost little to no credit information or history.
This platform makes use of thousands of data points and offers transparency which the other underwriting systems are not able to. This helps the lenders to better assess the populations which were traditionally considered “at risk.” The ZAML is an end-to-end platform that several institutions can implement and scale easily and efficiently.
DataRobot offers machine learning software for the data scientists, software engineers, business analysts, executives as well as IT professionals. DataRobot aids the financial institutions and businesses to quickly build accurate and predictive models that enhance the decision-making process around issues such as fraudulent credit card transactions, direct marketing, blockchain, digital wealth management, lending and much more.
3. SCIENAPTIC SYSTEM
In addition to other financial-based services, the Scienaptic Systems offers an underwriting platform that gives the banks and the credit institutions more transparency while cutting down their losses. Scienaptic’s Ether connects the myriad unstructured and structured data and smartly transforms the data, which allows you to learn from each interaction and also offers contextual underwriting intelligence.
Underwrite.ai analyzes thousands of data points from credit bureau sources to assess credit risk for the consumer and the small business loan applicants. This platform acquires the portfolio data and applies machine learning to find out the patterns and to determine the good and bad applications. Due to its accuracy, Underwriter.ai can reduce the number of defaults up to 25-50%.
Using AI for managing risk
In the finance world, time is money. But the risk can be deadly if it is not given the required attention. The accurate forecasts predictions are considered to be crucial for both the speed and the protection of many businesses. The Financial markets are turning extensively to machine learning, which is a subset of artificial intelligence, to provide nimbler models. These predictions aid the financial experts to utilize the existing data to pinpoint the trends, identify the risks, conserve the manpower and ensure better information for future planning.
The following examples reflect on how AI is assisting the financial and banking institutions to improve the predictions and to manage the risk.
Kensho offers machine intelligence as well as data analytics to the leading financial institutions such as Bank of America, J.P. Morgan, Morgan Stanley, and S&P Global. Kensho’s software provides analytical solutions by using a combination of cloud computing with natural language processing (NLP). The organization’s systems can give answers to complex financial issues in simple English language which can be understood by all very easily.
Ayasdi has created a cloud-based and on-premise machine intelligence solutions that can be used by enterprises and organizations to solve several complex financial challenges. For the companies in the fin-tech space, the solutions are deployed to understand as well as manage the risk, anticipate the needs of the customers and even help in the task of anti-money laundering processes.
Using AI for quantitative trading
Quantitative trading is said to be the process of using large data sets to identify the patterns which can be used to create strategic trades. The artificial intelligence is very useful in this kind of trading. AI-powered computers can help to analyze large and complex data sets in a faster and more efficient manner than humans can. The final algorithmic trading processes automate the trades and thus help to save on valuable time.
The following are a few examples of how AI-infused technology is aiding the financial institutions to make better trades.
An AI-powered search engine for the finance industry, the AlphaSense serves clients such as banks, investment firms, and several Fortune 500 companies. The platform utilizes natural language processing to analyze the keyword searches that exist within transcripts, filings, research and news to find out the changes and trends taking place in the financial markets. The search engine offers the brokers and traders access to SEC and the global filings, thus earning call transcripts, press releases along with information on the private and public organizations.
Kavout makes use of machine learning and quantitative analysis to process large sets of unstructured data and then identify the real-time patterns in the financial markets. One of the organization’s popular solutions is the Kai Score. It is an AI-powered stock ranker. It analyzes the massive amounts of data, like the SEC filings and price patterns, and then condenses the information to form a numerical rank for the stocks. The greater the Kai Score, the more likely that a stock will outperform in the market.
Alpaca combines the proprietary deep learning technology with high-speed data storage to offer short as well as long-term forecasting applications. The organization’s technology identifies the patterns in the market price-changes and then translates the findings into multi-market dashboards. The organization has recently partnered with the financial news giant Bloomberg that provides the users with the “AlpacaForecast AI Prediction Market.” This program predicts the short-term forecasts on a real-time basis for a majority of the markets.
Read More: Predicting Stock Market Pricing Using AI
Using AI for personalized banking
The traditional banking is not cutting it with the present-day digital-savvy consumers. A study conducted by Accenture of almost 33,000 banking customers discovered that 54% of the customers needed tools that would help them to monitor their budget and to make real-time spending adjustments. Also, 41% of the respondents were “very willing” to make use of computer-generated banking advice.
The AI assistants, like the chatbots, use artificial intelligence to required personalized financial advice as well as natural language processing to offer instant, self-help to the customers for the service.
Below mentioned are a few examples of organizations applying AI to learn from the customers and to create an enhanced banking experience.
The organization Kasisto is the creator of KAI, which is a conversational AI platform that is used to improve customer experiences in the financial sector. KAI helps the banks to reduce the call center volume by offering the customers with self-service options. Additionally, the AI-powered chatbots even provide the user’s calculated recommendations and assist with the other daily financial decisions. The TD Bank Group has announced plans to integrate with Kasisto’s technology into their mobile app, thus providing the customers with real-time spending insights and support.
11. ABE AI
The organization Abe AI is a virtual financial assistant who integrates with SMS, Google Home, Amazon Alexa, Facebook, web, and mobile to provide the customers with more convenient banking services. The assistant offers services ranging from a simple knowledge and support request to personal financial management and conversational banking experience.
Trim is said to be a money-saving assistant who connects to the user accounts and analyzes the spending pattern. This smart app can cancel several money-wasting subscriptions, find out better options for numerous services such as mutual funds, fixed deposit investments, insurance, and even help to negotiate bills.
Using AI for cybersecurity & fraud detection
The need to ramp up the cybersecurity and fraud detection efforts is a necessity for any bank or financial institution. Here artificial intelligence is playing a primary role to improve the security of online financial activities.
Below mentioned are a few examples of organizations providing AI-based cybersecurity solutions for several financial institutions.
13. SHAPE SECURITY
Shape Security curbs credit application fraud, credential scraping, and gift card cracking by pinpointing the fake users. The organization’s machine learning models have been trained on billions of requests, letting the software effectively distinguish between the real consumers and the bots.
The company’s machine learning platform analyzes the network data and then creates probability-based calculations, thus detecting the suspicious activity even before it can cause any kind of damage for the financial firms.
Vectra’s platform automates the threat detection, reveals the hidden attackers especially targeting the financial institutions, accelerates the investigations after the incidents, and identifies the compromised information.
Read More: Can AI Transform Cybersecurity?
These examples highlight the use of AI in the finance industry.