There’s no doubt that the finance industry is undergoing a transformational change. To provide superior service to their customers and outdo the competition, financial institutions are using the latest technologies to understand customer needs, identify opportunities, stay competitive, solve business problems and streamline back office operations,
The recent years have seen a rapid acceleration in the pace of disruptive technologies such as AI and Machine Learning in Finance due to improved software and hardware. The finance sector, specifically, has seen a steep rise in the use cases of machine learning applications to advance better outcomes for both consumers and businesses.
Machine Learning Use Cases in Finance
Here are a few use cases where machine learning algorithms can be/are being used in the finance sector –
1.Financial Monitoring
Machine learning algorithms can be used to enhance network security significantly. Data scientists are always working on training systems to detect flags such as money laundering techniques, which can be prevented by financial monitoring. The future holds a high possibility of machine learning technologies powering the most advanced cybersecurity networks.
2.Making Investment Predictions
The fact that machine learning-enabled technologies give advanced market insights allows the fund managers to identify specific market changes much earlier as compared to the traditional investment models.
With renowned firms such as Bank of America, JPMorgan, and Morgan Stanley investing heavily in ML technologies to develop automated investment advisors, the disruption in the investment banking industry is quite evident.
3.Process Automation
Machine Learning powered solutions allow finance companies to completely replace manual work by automating repetitive tasks through intelligent process automation for enhanced business productivity. Chatbots, paperwork automation, and employee training gamification are some of the examples of process automation in finance using machine learning. This enables finance companies to improve their customer experience, reduce costs, and scale up their services.
Further, Machine Learning technology can easily access the data, interpret behaviors, follow and recognize the patterns. This could be readily used for customer support systems that can work similar to a real human and solve all of the customers’ unique queries.
An example of this is Wells Fargo using ML-driven chatbot through the Facebook Messenger to communicate with its users effectively. The chatbot helps customers get all the information they need regarding their accounts and passwords.
4.Secure Transactions
Machine Learning algorithms are excellent at detecting transactional frauds by analyzing millions of data points that tend to go unnoticed by humans. Further, ML also reduces the number of false rejections and helps improve the precision of real-time approvals. These models are generally built on the client’s behavior on the internet and transaction history.
Apart from spotting fraudulent behavior with high accuracy, ML-powered technology is also equipped to identify suspicious account behavior and prevent fraud in real-time instead of detecting them after the crime has already been committed.
According to a research, for almost every $1 lost to fraud, the recovery costs borne by financial institutions are close to $2.92.
One of the most successful applications of ML is credit card fraud detection. Banks are generally equipped with monitoring systems that are trained on historical payments data. Algorithm training, validation, and backtesting are based on vast datasets of credit card transaction data. ML-powered classification algorithms can easily label events as fraud versus non-fraud to stop fraudulent transactions in real-time.
5.Risk Management
Using machine learning techniques, banks and financial institutions can significantly lower the risk levels by analyzing a massive volume of data sources. Unlike the traditional methods which are usually limited to essential information such as credit score, ML can analyze significant volumes of personal information to reduce their risk.
Various insights gathered by machine learning technology also provide banking and financial services organizations with actionable intelligence to help them make subsequent decisions. An example of this could be machine learning programs tapping into different data sources for customers applying for loans and assigning risk scores to them. ML algorithms could then easily predict the customers who are at risk for defaulting on their loans to help companies rethink or adjust terms for each customer.
6.Algorithmic Trading
Machine Learning in trading is another excellent example of an effective use case in the finance industry. Algorithmic Trading (AT) has, in fact, become a dominant force in global financial markets.
ML-based solutions and models allow trading companies to make better trading decisions by closely monitoring the trade results and news in real-time to detect patterns that can enable stock prices to go up or down.
Machine learning algorithms can also analyze hundreds of data sources simultaneously, giving the traders a distinct advantage over the market average. Some of the other benefits of Algorithm Trading include –
- Increased accuracy and reduced chances of mistakes
- AT allows trades to be executed at the best possible prices
- Human errors are likely to be reduced substantially
- Enables the automatic and simultaneous checking of multiple market conditions
7.Financial Advisory
There are various budget management apps powered by machine learning, which can offer customers the benefit of highly specialized and targeted financial advice and guidance. Machine Learning algorithms not only allow customers to track their spending on a daily basis using these apps but also help them analyze this data to identify their spending patterns, followed by identifying the areas where they can save.
One of the other rapidly emerging trends in this context is Robo-advisors. Working like regular advisors, they specifically target investors with limited resources (individuals and small to medium-sized businesses) who wish to manage their funds. These ML-based Robo-advisors can apply traditional data processing techniques to create financial portfolios and solutions such as trading, investments, retirement plans, etc. for their users.
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When hearing ‘AI for Customer Service’ most would immediately think this is going to be ‘yet another article about chatbots’. No wonder. Chatbots have been one of the hottest topics recently. However, our experience shows that AI can be applied to many other areas of Customer Service business as well. And those possess even more potential to boost your business.
Best Customer Service Chatbots To Relieve 20-30% Of The Load
As the hottest topic in 2017, AI support bots were expected to be a game changer. And even though the ebullience faded a bit when it became clear that even best customer service chatbots still have a lot to learn, a properly designed and trained chatbot can substantially relieve the load on 1-st line support.
As statistical data show, 87% of chatbot users worldwide consider them efficient or very efficient in resolving issues.
As you see AI and ML benefits for customer service are already obvious. AI and ML are definitely changing the rules of the game in the customer support industry and the use of AI/ML technology in this industry will only grow.
However, AI and ML are rather additional tools, which can amplify but not replace traditional customer service automation software and savvy approach to automation. No matter how intelligent your systems are – they are just the reflection of your organization and your processes, which should never be neglected.
