Artificial Intelligence is revolutionising the financial industry, and financial institutions find innovative ways to incorporate technology into their services. AI, NLP and ML in finance are all about task automation, fraud detection, delivering personalised recommendations, risk management, increased user acceptance, streamlining complicated processes and enhancing customer experience.
AI algorithms are being implemented by financial institutions – whether in personal finance, consumer finance or corporate finance.
Fintech: The Future of AI in Finance
The millennials and Gen Zs are rapidly adopting Artificial Intelligence and Machine Learning which has revolutionised the world of finance. An AI system can scrutinise millions of data points, predict future patterns and find trends that people may miss. Artificial Intelligence, along with natural language processing, has entered the finance industry and changed the following:
- Process Automation – AI helps in personalising financial reports after comprehending the rules and regulations. There is no need to seek professional support to derive answers to complicated laws on finance. AI also generates more in-depth status reports on clients, allowing the finance industry to give them customised advice on their financials. For example, IBM’s Watson can understand complex regulations regarding requirements of the markets in financial instruments and home mortgage.
- AI helps automate the decision-making process by assessing the financial details of clients based on their spending pattern, payment history, associated risks, etc.
- Automation for saving and budgeting has become much easier with AI in finance by analysing individual accounts and seeing how they have performed financially, making recommendations for future actions based on results.
- AI can promote advanced analytics and provides an easy way to answer integral questions. It can be used to examine credit accounts, cash accounts and investment accounts to look at a person’s financial health.
- AI in finance helps in detecting fraudulent activities, which have tremendously increased. For example, Transaction Data Enrichment uses AI and ML to decipher hard-to-understand data to find complete personal and financial information about the users. This includes elements like the name, location, and other specified details of the user, along with the source of their payment to verify the authenticity of the customer. This technique of AI in eCommerce provides pivotal information about local merchants and every customer who uses the platform.
- AI in finance is commendably used for personalized banking, wherein customers can be constantly reminded about their financial data, interest rates, different account features, and data-driven lending.
- For example, financial forecasting tools guide users relying on AI on how they can spend money, depending on their budget, upcoming obligations, bank balances, etc. AI, along with ML and predictive analysis, can help users by predicting future financial outcomes.
- eCommerce in finance turns to AI to predict shopping patterns, gain better analytical insights, mitigate operating expenses and stay ahead of competitors.
How is AI in Finance Changing eCommerce Businesses?
1. Risk Assessment
AI is extremely beneficial in the finance department to determine a person’s or an organization’s risk factor. The best feature about introducing AI in risk assessment is that it is an unprejudiced and unbiased technology that can make quick and accurate decisions about factors that need to be thoroughly monitored and controlled.
- Risk mitigation is essential and has always been a challenging task in the finance sector. AI and Machine Learning can help professionals use data to identify trends, conserve manpower, analyze risks, and ensure more prudent and critical information for future planning.
- AI and Machine learning handle and analyze unstructured data, which eventually helps in lowering operational, compliance, and regulatory costs, saving time and money for financial services companies, and providing reliable credit scoring.
- Cognitive computing in AI helps manage all kinds of data – whether structured or unstructured in finance. Algorithms analyze the history of risk cases and identify signs of future issues.
2. AI in Payment
AI has encouraged transaction banking by understanding many processes, supporting a large number of customer interactions, generating large volumes of data, and eventually making transaction banking a great platform for AI. AI in finance within eCommerce helps in understanding payment trends, online transactions, and history/capacity/ and ability of online consumers.
For example, Barclays Corporate has used AI technologies such as cognitive reasoning, deep learning, machine learning, robotic process automation, and natural language processing. All these techniques have helped the company to automate mundane tasks. Further, AI techniques have helped it to extract unusual financial behavior among the staff, give information about the payment that is not recognized, flag unusual transactions, and verify individual financial information.
3. AI in Fraud Detection (Finance)
AI in fraud detection is an excellent application for machine learning, having a track record of success in banking, insurance, and eCommerce. AI can detect fraud by analyzing huge numbers of transactions, flagging suspected transactions for further investigation, finding the faults, and speeding up the investigation.
Machine learning can identify patterns in data sets by creating analytic models without the assistance of a human analyst. This automatic finding of patterns used in many streaming transactions is highly successful in fraud prevention and detection.
4. AI in Revenue Management
AI, ML, and NLP are helping the finance industry to improve pricing and revenue management:
- Using AI to identify or eliminate the most unproductive customer discounts and segments. For example, Bain & Company used AI to analyze discounts to stop revenue leakage and expensive customer investments.
- Using AI to automate pricing rules. For example, Boston Consulting Group improved pricing optimization with advanced AI techniques and delivered an optimum increase in revenue.
Other ways that AI has enhanced the revenue management in finance, changing eCommerce businesses are:
- Looking for patterns in pricing, volume, and mix analysis.
- Helping pricing managers earn a higher ROI by optimizing prices within their product and customer mix.
- Predicting which customers will act on bundling or pricing offers.
- Helping in price optimization and price elasticity.
- Improving Configure, Price, Quote (CPQ) effectiveness.
- Monitoring risk-based metrics and providing accurate deal price guidance to gain better insight into revenue-building and the root cause of potential risks.
5. AI in Preventing Cyber Attacks
Financial organizations should be at ease and have confidence in their systems to keep their information safe and secure. Artificial Intelligence can help and recover 95% of breaches and errors caused by human intervention and error. AI alerts companies of unusual activities, immediate discrepancies and boosts financial security by normalizing data trends and patterns.
6. Forecasting and Analytics
AI helps financial companies derive more accurate forecasts for stock management, sales, and marketing outcomes and make more informed decisions. It helps the companies’ gauge where their potential customers come from, what age group they fall into, and their interests. AI helps to enhance potential lead generation by discovering clients and marketing to them. It is useful for predicting the success of innovations and can use historical data to improve results after implementing specific changes.
7. Dynamic Pricing
AI for eCommerce can render financial intelligence from the competition and generate fluctuations in pricing and base pricing suggestions. It can also discover how other companies adjust their product mix, lure customers, advertise, market new products and services, and change their product assortment.
As highlighted in this article, artificial intelligence in Ecommerce is playing a leading role in driving innovative solutions and customer experiences. Some of the leading use cases of artificial intelligence in Ecommerce are in the area of personalized shopping, product recommendations, and inventory management.
One Such platform is RecoSense, which offers AI Personalization solutions for Retail brands to improve user engagement, conversions, and AOV. Enable instant recommendations and marketing offers for eCommerce, Shopify, Magento stores with Unique Buyer Persona and contextual insights.
AI in finance is transforming the eCommerce business. It is a powerful tool that allows companies to leverage big data to identify new prospective target audiences, monitor competitors’ activity, learn about customers’ buying patterns, and achieve new levels of optimization. It is recommended to start small and slow and integrate AI as a staple in key eCommerce processes after building a strong foundation.