Artificial intelligence (AI) is revolutionizing the transformation of interaction with money, and how consumers and companies alike access and manage their finances. AI in finance encompasses everything from chatbot assistants to fraud detection and task automation.

AI is helping the financial industry to streamline and optimize processes ranging from credit decisions to quantitative trading and financial risk management. The decision for financial institutions to adopt AI will be accelerated by technological advancement, increased user acceptance, and shifting regulatory frameworks.

I’ve put together a rundown of how Artificial Intelligence can be used in finance leading the way.

Credit Decisions

The majority of the consumers preferred paying with a debit or credit card compared to those who favored cash.

AI solutions can help banks and credit lenders make smarter underwriting decisions by utilizing a variety of factors that are more accurately assess traditionally underserved borrowers, like millennials, in the credit decision making process. Let’s find out how?

1. AI-powered underwriting solutions can help companies assess borrowers with little to no credit information or history. The end-to-end platform can use thousands of data points and provide the transparency that other underwriting systems cannot, which helps lenders better assess populations traditionally considered “at risk.”
2. Accurate predictive models can enhance decision making around issues like fraudulent credit card transactions, digital wealth management, direct marketing, blockchain, lending, and more.
3. The machine learning model can provide an underwriting platform that gives banks and credit institutions more transparency while cutting losses by analyzing tons of data points from sources to assess credit risk.

Risk Management

Time is money in the finance world, but the risk can be deadly if not given the proper attention. Accurate forecast predictions are crucial to both the speed and protection of many businesses. Here’s how AI can help financial and banking institutions improve predictions and manage risk.

1. Analytical solutions using a combination of cloud computing and Natural Language Processing (NLP), and the system can provide answers to complex financial questions in plain understandable English or any other language of choice.
2. Cloud-based and on-premise machine intelligence solutions for enterprises and organizations can solve complex challenges, understand and manage risk, anticipate the needs of customers, and even combat money laundering.

Quantitative Trading

Quantitative trading is the process of using large data sets to identify patterns that can be used to make strategic trades. AI-powered systems can analyze large, complex data sets faster and more efficiently than humans.

The resulting algorithmic trading processes can automate trades and save valuable time. Let’s discuss how AI-infused technology can help financial institutions make better trades.

1. AI-powered search engine for the financial industry where the platform can use Natural Language Processing (NLP) to analyze keywords searches within filings, transcripts, research, and news to discover changes and trends in the financial market.
2. Machine learning and quantitative analysis can process vast sets of unstructured data and identify real-time patterns in financial markets with an AI-powered stock ranker. It can analyze massive amounts of data, such as filings and price patterns, then condense the information into a numerical rank for stocks. The higher the score, the more likely the stock will outperform the market.
3. Deep learning and high-speed data storage to provide short and long-term forecasting applications that can identify patterns in market price-changes and translates its findings into multi-market dashboards.

Personalized Banking

Traditional banking solutions aren’t helping with today’s tech-savvy consumers. AI assistants, such as chatbots, use Artificial Intelligence to generate personalized financial advice and Natural Language Processing to provide instant, self-help customer service. Here are a few ways of how AI can create a better banking experience for customers.

1. A conversational AI can improve customer experiences in the finance industry by reducing call center volume, by providing customers with self-service options and solutions. It can also give users calculated recommendations and help with other daily financial decisions.
2. A virtual financial assistant that integrates with Google Assistant, Apple Siri, and Amazon Alexa, web and mobile to provide customers with more convenient banking. The assistant can provide services ranging from simple knowledge and support requests to personal financial management and conversational banking.
3. A money-saving assistant can connect to user accounts and analyze spending, and the application can cancel money-wasting subscriptions, find better options for services like insurance, and even negotiate bills.

Cybersecurity & Fraud Detection

A vast amount of digital transactions take place via online accounts and mobile applications. Cybersecurity and fraud detection is now a necessity for any bank or financial institution, and Artificial Intelligence can play a key role in improving the security of online finance.

Here are a few AI-based cybersecurity solutions for any financial institutions.

1. AI can curb credit application fraud, credential stuffing, scraping, and gift card cracking by pinpointing fake users. The machine learning models can be trained on billions of requests, allowing the software to effectively distinguish between real consumers and bots. AI-enabled bots can detect compromised login credentials, alerting both customers and companies to security breaches instantly.
2. The machine learning platform can analyze network data and creates probability-based calculations, detecting suspicious activity, and sophisticated cyber-attacks before it can cause damage.
3. AI-powered cyber-threat detection and hunting solutions which can automate threat detection, reveal hidden attackers specifically targeting financial institutions, accelerate investigations after incidents, and even identify compromised information