Machine Learning – applications, use-cases, and current presence within banking and finance Part 2
This is the second part of AICorespot’s deep dive into Machine Learning, its applications, use-cases, and current presence within the banking and financial space. In the previous section, we saw the inroads that Machine Learning and Artificial Intelligence are making with regards to tackling fraud, ML-based detection is much better off than legacy rules-based detection in detecting and averting fraud, even before it happens.
In the second part of this blog series by AICorespot, we will be looking at the role of Artificial Intelligence and Machine Learning with regards to ever-evolving regulatory requirements, legislation, and compliance. We will also be looking at how Artificial Intelligence and Machine learning facilitates client experiences, and how financial enterprises can tailor their offerings towards customers based on big data and analytics with regards to preferences. Machine learning can detect patterns and preferences, likes and dislikes, using analysis of customer behaviour that has been mined.
We will also be looking at the kind of impact that Machine Learning and Artificial Intelligence has had on the stock market, and how it has facilitated and streamlined the fine art of making calculated financial predictions.
ML within the banking domain and finance assists enterprises with constantly-evolving regulations
In the present scenario, financial enterprises are recording expenditures to the tune of billions of dollars with regards to regulatory compliance, a dominant proportion of them will have to negotiate with additional rules and regulations. Concentration on regulatory issues with the banking and FinTech space needs a lot of time investment and monetary resources. Further, making that investment cannot ensure that all rules are adhered to in a proactive and timely fashion.
Leading machine learning use-cases within finance are applications encompassed under the categorization of Regulatory Technology (RegTech). As ML-based algorithms can interpret an go about learning from a pile of documentation with regards to regulation, they have the potential to identify correlations amongst guidelines. Cloud platforms featuring integrated machine learning algorithms leveraged in finance can, on an automatic basis, track and survey regulatory changes as they appear. Banking enterprises can additionally survey and go about monitoring
transaction data to detect anomalies on an automatic basis. In this fashion, ML can make sure that client transactions are in compliance with regulatory requirements.
What part does ML play in regulatory compliance?
Advantages of regulatory compliance
- Banking enterprises can conform in a better manner with relevant legislation, regulatory standards, and supervisory requirements.
- Time-intensive and usually complicated tasks can be carried out by machines rather than humans.
- Regulatory work can be performed quicker with reduced risks of non-compliance, minimizing several manual tasks.
ML enriches the end-user experience for clients
There are various reasons why individuals select FinTech services rather than conventional variants. Taking into account machine learning’s capacity to deep dive into petabytes of big data to identify precisely what makes a difference to a specific client, harnessing this insight, financial enterprises can tailor customized offerings to their clients, and this speaks to the pulse of the client, greatly assisting in retention and preventing customer churn. Customers love the feelings of being heard and understood, and Artificial Intelligence and Machine Learning technologies greatly facilitate and drive this.
How do machine learning and artificial intelligence platforms integrate into the framework of client service infrastructure?
Another instance of an impactful ML use-case within the banking space is the widespread proliferation of chatbots within industry. ML facilitates a brand new generation of chatbots that are intrinsically smart, resemble humans, and are very client-aligned. As chatbots are trained and undergo learning with every particular piece of interaction, the conversations they have become a lot more beneficial and personalized. There is reduced requirement to develop or expand client service departments, and this is a massive advantage, particularly for small- and mid-sized financial operators.
Chatbots dominated the client service interaction landscape for the previous few years, and this established dominance is expected to continue in 2022 and beyond.
AI and ML platforms within the context and framework of client service infrastructure
Advantages of ML with regards to client service infrastructure
- Appreciation in revenues owing to enhanced user experiences and improved productivity
- Enterprises that leverage machine learning for sophisticated client service are looked at as being more in tune with the current scenario.
- Customers value innovation-driven FinTech enterprises that streamline their everyday lives and inject actual value.
ML can create a paradigm shift within the stock market
In what way is machine learning harnessed in the financial prediction game of the stock market? Undoubtedly a high-stakes game, the massive volumes of trading ops has the outcome of terabytes upon terabytes of historical data – presenting an unrestricted prospect with regards to learning. Still, historical information is just the grounds on which such calculated forecasts are made. ML algorithms look into data sources that are available on a real-time basis, like trade outcomes and news, to highlight patterns signifying the underlying dynamics of the stock market. The task that traders are faced with is to decide which machine learning algorithm to integrate into their working techniques, make a trading prediction, and select a behavioural pattern.
A conventional workflow with regards to trading frameworks leveraging supervised learning
Advantages of Machine Learning within the Stock Market
- The predictive capabilities of machines are limitless, far surpassing those of human beings.
- Machine learning can identify the most incremental fluctuations in pricing.
- Machine learning makes it easy to contrast data spanning across various decades.
- ML algorithms can undertake trading decisions in a lightning-fast manner.
- ML algorithms are devoid of the bias that is a feature of interpretations originating from the human intellect.