12 Use Cases of AI and Machine Learning ML In Finance

This can help increase customer satisfaction while increasing revenues for the financial institution. For example, a company can offer car insurance to its customer who is in the process of buying car. Oliver Wyman shares that using AI insights can increase annual income from email cross-sell by four times. This computer-vision-based technology is relatively simple – the payment terminal scans your face, sending its template to the interpreting device that compares it with the verified template from your bank. The process takes a few seconds and provides the customer with a new level of convenience since the transaction doesn’t require a mobile phone or a credit card.

As for the rationale behind artificial intelligence applications in finance, it is used for financial decision-making for a few reasons. First, traditional software struggles with the complexity of financial products and the volatility of markets. To process real-time data, banks need to switch from legacy technology to machine learning solutions. Various insights gathered by machine learning technology also provide banking and financial services organizations with actionable intelligence to help them make subsequent decisions.

Personalized Banking ‌

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. Leading FinTech companies like JP Morgan have made it clear that the future of customer-centric financial services lies in crunching vast amounts of data drawn from varied sources—often non-traditional. Morgan has recently summarized critical research in machine learning, big data, and artificial intelligence, highlighting exciting trends that impact the financial community. Although the integration of AI into finance needs further development, the benefits definitely outweigh the potential costs.

As banking is documentation-intensive per se, such innovation makes quite a difference. Kasisto are already developing industry-specific software intended for banks and other financial organizations. Such software will help customers make the necessary calculations and evaluate their budgets quickly. The advantages How Is AI Used In Finance of AI become obvious when it comes to personalization and providing additional benefits for users. For instance, banks use AI-powered chatbots to offer timely help while also minimizing the workload of their call centers. Perhaps, the main advantage of AI is that it gives countless automation opportunities.

Growing a Business

AI technology brings the advantage of digitization to banks and helps them meet the competition posed by FinTech players. About 32% of financial service providers globally are already using AI technologies like Predictive Analytics, Voice Recognition, etc, according to joint research conducted by the National Business Research Institute and Narrative Science. Another report suggests that by 2023, banks are projected to save $447 billion by using AI mobile apps. These numbers indicate that the banking and finance sector is swiftly moving towards AI to improve efficiency, service, productivity, and RoI and reduce costs. According to a survey of financial services professionals, 80 percent of banks are highly aware of the potential benefits presented by AI.

The company reported that auto lenders using machine-learning underwriting cut losses by 23 percent annually, more accurately predicted risk and reduced losses by more than 25 percent. Having good credit makes it easier to access favorable financing options, land jobs and rent apartments. So many of life’s necessities hinge on credit history, which makes the approval process for loans and cards important. And as the market expands, it’s important to know some of the companies leading the way. It should be noted, however, that the risk of discrimination and unfair bias exists equally in traditional, manual credit rating mechanisms, where the human parameter could allow for conscious or unconscious biases. The use of the term AI in this note includes AI and its applications through ML models and the use of big data.

Three emerging priorities for CMOs at banks

In the background, an AI-based solution verifies if there is a match, but at the same time checks if the ID is not fake and if there is nothing alarming with the picture. Working with images is where Deep Learning and architectures such as Convolutional Neural Networks show very promising results. The banking sector tries to harness the power of AI to provide a personalized banking experience for everyone.

Automated solutions for financial sales already exist, but not all of them involve machine learning. For instance, they are already capable of making suggestions on possible changes to the portfolio, but they can also analyze various websites with recommendations on insurance services and help you choose a plan that meets your objectives. Robotic process automation algorithms increase operational efficiency and accuracy and reduce costs by automating time-consuming repetitive tasks.

Use of artificial intelligence in accounting and finance

Encourage financial services providers to test digital disclosure approaches to ensure their effectiveness and recognise that there may be consumers in the target audience for the product or service who are not digitally literate. It notably calls on policy makers to increase awareness among consumers of the analytical possibilities of big data and of their rights over personal data, for them to take steps to manage digital footprints and protect their data online. The OECD has undertaken significant work in the area of digitalisation to understand and address the benefits, risks and potential policy responses for protecting and supporting financial consumers. The OECD has done this via its leading global policy work on financial education and financial consumer protection. The G20 Riyadh Infratech Agenda, endorsed by Leaders in 2020, provides high-level policy guidance for national authorities and the international community to advance the adoption of new and existing technologies in infrastructure. One bank taking advantage of AI in consumer finance is JPMorgan Chase.For Chase,consumer bankingrepresents over 50% of its net income; as such, the bank has adopted key fraud detecting applications for its account holders.

Banks and financial institutions have continuously adopted technology to stay relevant and offer improved services to their customers. In the AI age, finance and banking will have become AI-first rather than use AI technology on their periphery. With the correct implementation, they can improve human decision-making and reduce risk, unlocking a trillion-dollar opportunity for this industry. What was traditionally a people-heavy industry with loads of analysts and money managers, financial services has slowly transformed into a lean technology-heavy behemoth. As a result, we are looking at augmented human intelligence using AI, resulting in greater efficiency, reduced costs for banking institutions and new offerings to consumers. Using machine learning techniques, banks and financial institutions can significantly lower the risk levels by analyzing a massive volume of data sources.

How Accurate Is the Application of AI in Banking?

Complying with regulatory requirements is essential for banks and other financial institutions. AI can leverage Natural Language Processing technologies to scan legal and regulatory documents for compliance issues. As a result, it is a scalable and cost-effective solution because AI can browse thousands of documents rapidly to check non-compliant issues without any manual intervention.

• In the past, compliance officers were tasked with digging through various communication channels, searching for anything unethical and/or unlawful.
• With AI poised to handle most manual accounting tasks, the development and proficiency of higher-level skills will be imperative to success for the next generation of finance leaders.
• This helped our client to reduce manual processes by 35% and improve accuracy by 50%.
• According to a survey of financial services professionals, 80 percent of banks are highly aware of the potential benefits presented by AI.
• Forbes reports that traditional market research is not only costly and slow but is also a closed resource that blocks the advancement of knowledge.
• To test the prototypes, banks need to compile relevant data and feed it to the algorithm.

For example, a customer looking to invest in a financial plan can be benefitted from a personalized investment offer after the ML algorithm analyses his/her existing financial situation. 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. Further, machine learning algorithms are equipped to learn from data, processes, and techniques used to find different insights.

AI applications in the fintech industry range from recognizing abnormal transactions to identifying suspicious and potentially fraudulent activities by analyzing massive amounts of data. AI can quickly gain insights that help protect organizations against losses and increase ROI for their customers. It allows financial institutions to leverage vast amounts of data to extract more insights, automate repetitive tasks, and accelerate innovation. However, there is still a long way for AI models to be widely used in financial services. AI models could take into account variables like gender, race, or profession which may have been used historically in credit applications. The applications listed above may be spectacular, but let’s not forget that artificial intelligence supports the banking institutions behind the scenes, too.

However, the use-cases of AI in finance are not restricted to ML models for decision-making and expand throughout the spectrum of financial market activities (Figure 2.1). Research published in 2018 by Autonomous NEXT estimates that implementing AI has the potential to cut operating costs in the financial services industry by 22% by 2030. Artificial Intelligence is the future of banking as it brings the power of advanced data analytics to combat fraudulent transactions and improve compliance. AI algorithm accomplishes anti-money laundering activities in few seconds, which otherwise take hours and days. AI also enables banks to manage huge volumes of data at record speed to derive valuable insights from it. Features such as AI bots, digital payment advisers and biometric fraud detection mechanisms lead to higher quality of services to a wider customer base.

Many institutions reach out for it to maximize sales by improving the efficiency of cross-selling. The predictive models can learn with existing customer behavior data to come up with more relevant cross-selling offers for a particular client. With a thorough segmentation of the dataset and market basket analysis, the suggestions may become even more accurate, making sales skyrocket.

• It focuses on data-related issues, the lack of explainability of AI-based systems; robustness and resilience of AI models and governance considerations.
• When we think about artificial intelligence, perhaps we imagine the Terminator movies and intelligent robots with human-like behaviors.
• Artificial intelligence can boost company security by analyzing and determining normal data patterns and trends, and alerting companies of discrepancies or unusual activity.
• Weak AI does not fully encompass intelligence rather it focuses on completing a particular task it is assigned to complete.
• The technology is being used to provide financial advisors with potential outcomes and minimize risk.
• By no means are the lists exhaustive, as both the AI and financial landscapes change constantly and adapt to the progress that is made on a daily basis.

Biases may also be inherent in the data used as variables and, given that the model trains itself on such data, it may perpetuate historical biases incorporated in the data used to train it. AI-driven systems may exacerbate illegal practices aiming to manipulate the markets, such as ‘spoofing’6, by making it more difficult for supervisors to identify such practices if collusion among machines is in place. While the latest state-of-art neural network architecture may be appealing and provide better accuracy, it’s rarely the best tool for the job due to its complex nature. However, when the number of characteristics skyrockets, many machine learning approaches start to struggle. In that case, the analysts must either carry out some kind of feature selection or attempt to minimize the data’s dimensionality.