Artificial Intelligence in Banking 2022: How Banks Use AI

Interestingly, while some Western countries didn’t embrace these opportunities, others – including the countries of the former Eastern Bloc, like Poland or Estonia – have become the leaders of innovation in banking. In recent years, their financial institutions have introduced innovative tools and strategies in their mobile apps and customer service, setting new European or even global standards. AI is also being implemented by banks within middle-office functions to assess risks, detect and prevent payments fraud, improve processes for anti-money laundering and perform know-your-customer regulatory checks. Besides, voice recognition enables banks to provide assistance in the most convenient way possible. Solutions that are based on machine learning require little to no assistance from humans.

In most cases, banks maintain an internal compliance team to deal with these problems, but these processes take a lot more time and require huge investment when done manually. The compliance regulations are also subject to frequent change, and banks need to update their processes and workflows following these regulations constantly. Retail, business, and investment banks make up the lion’s share of the finance sector.

Changes to the loan system

According to Crowe, fraud chews up $5 trillion from the global economy each year, and that number continues to rise. Traditional fraud prevention methods are giving way to How Is AI Used In Finance machine learning, which showcases greater efficiency at the job. To detect even the slightest risk, a company needs to analyze every single transaction in large data sets.

AI finance tools can outperform human trades and bring faster and better decisions on trading. Also, the comprehensive analysis of different market aspects and factors allows banks to achieve new heights in trading algorithms. The technology is quite popular for data science as it helps a company build its trading system. Banks and financial institutions are combining AI with other emerging technologies to drive game-changing transformation. Forbes reports that already “70% of all financial services firms are using machine learning to predict cash flow events, fine-tune credit scores and detect fraud” . There is not a single industry that has been left untouched by the transformative impact of artificial intelligence technology in the last decade — financial services is no exception.

Technology

Darktrace creates cybersecurity solutions for a variety of industries and financial institutions are no exception. Kasisto is the creator of KAI, a conversational AI platform used to improve customer experiences in the finance industry. Here are a few examples of companies using AI to learn from customers and create a better banking experience. The following companies are just a few examples of how AI-infused technology is helping financial institutions make better trades. Kensho, an S&P Global company, provides machine intelligence and data analytics to leading financial institutions like J.P. Underwrite.ai analyzes thousands of data points from credit bureau sources to assess credit risk for consumer and small business loan applicants.

• Ultimately, the use of AI could support the growth of the real economy by alleviating financing constraints to SMEs.
• The impact of AI in the banking and financial sector has been phenomenal and it is completely redefining the way they function, create products and services, and how they transform the customer experience.
• Promote practices that will help overcome risk of unintended bias and discrimination.
• Financial institutions sit on treasure troves of data, as a single transaction can have thousands of data points.
• A minimum level of explainability would still need to be ensured for a model committee to be able to analyse the model brought to the committee and be comfortable with its deployment.
• Minor inconsistencies in AI systems do not take much time to escalate and create large-scale problems, thereby risking the bank’s reputation and functioning.

It should be clean and well-maintained data for machine learning input, which is a challenge to get. In many industries, data scientists are very eager to use the latest and greatest state-of-the-art techniques which perform tons of complex calculations under the hood and provide very accurate predictions. While in many cases this can be a reasonable thing to do, there is more to it in finance. Financial institutions sit on treasure troves of data, as a single transaction can have thousands of data points. That is also why there is a very low signal-to-noise ratio in the industry, which makes the work of data scientists very challenging and interesting at the same time.

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Automating middle-office tasks with AI has the potential to save North American banks $70 billion by 2025. Further, the aggregate potential cost savings for banks from AI applications is estimated at $447 billion by 2023, with the front and middle office accounting for $416 billion of that total. Artificial intelligence is revolutionizing how consumers and companies alike access and manager their finances.

It can also amplify network effects, such as unexpected changes in the scale and direction of market moves. For example, let’s consider a person who has a low credit score and has their loan application denied. The individual could then file a claim and request a detailed explanation of all the factors that led to the rejection. There must be a mechanism to instantly locate anomalies throughout the entire pipeline, pinpoint the problem, and resolve it. That’s exactly why some businesses are built around this idea and offer git-like version control for even their own data.

Trading

Many open-source toolkits such as IBM AI Fairness 360, Aequitas, and Google What-if assist fintech companies in measuring discrimination in AI models. They recommend mitigation pathways to eliminate bias from data pipeline, and test the overall impact of the biased data on real-world scenarios. Artificial intelligence offers financial institutions a strategic opportunity provided they invest in data quality management. This would naturally increase customer retention and satisfaction by instilling trust through a secure and seamless authentication process. Some of the companies that have heavily invested in security machine learning and are working extensively towards this shift include Adyen, Payoneer, Paypal, and Stripe. Machine learning applications in the finance sector are likely to take security to the next level through the use of voice and face recognition, as well as other biometric data.

Facial recognition payments are widely available in China in self-service stores or restaurants . Their systems process such payments, associating it with a customer DNA to track the client’s preferences and provide them with more accurate recommendations every single time. Aside from default risk, predictive algorithms can also estimate the interest rate risk and prepayment risk, which are essential variables in loan underwriting. Using supervised learning , the banks can automate their loan risk assessment process and credit scoring, making the processing of the loan application much faster and more effective. Cognitive systems that think and respond like human experts, provide optimal solutions based on available data in real-time.

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Data is the cornerstone of any AI application, but the inappropriate use of data in AI-powered applications or the use of inadequate data introduces an important source of non-financial risk to firms using AI techniques. Such risk relates to the veracity of the data used; challenges around data privacy and confidentiality; fairness considerations and potential concentration and broader competition issues. As the responsibility of data curation shifts from third party nodes to independent, automated AI-powered systems that are more difficult to manipulate, the robustness of information recording and sharing could be strengthened. In a hypothetical scenario, the use of AI could further increase disintermediation by bringing AI inference directly on-chain, which would render Oracles redundant.

Quantitative trading is the process of using large data sets to identify patterns that can be used to make strategic trades. Time is money in the finance world, but risk can be deadly if not given the proper attention. One report found that 80 percent of consumers prefer spending with their debit or credit card over cash. They can be external service providers in the form of an API endpoint, or actual nodes of the chain.

• In today’s era of digitization, staying updated on technological advancements is a necessity for businesses to both outsmart the competition and achieve desired business growth.
• The latter cripple the serene existence of financial businesses and negatively affect revenues.
• AI is expected to serve as a vehicle for customer-centric services in the finance industry.
• They are also fairly costly due to the significant labor costs involved with them.
• Withnatural language processing , the bots can extract information and capture knowledge from documents in order to facilitate application processing and decision-making.
• The best example of weak AI is Apple’s Siri which is governed by the substantial database of the internet.

From the lack of credible and quality data to security issues, a number of challenges exist for banks using AI technologies. After identifying the potential AI and machine learning use cases in banking, the technology teams should run checks for testing feasibility. As of today, banking institutions successfully leverage RPA to boost transaction speed and increase efficiency.

• Chatbots are one of the best examples of practical applications of artificial intelligence in banking.
• One of the struggles AI faces is accountability, which surfaces mistrust in the outputs from AI.
• Check out the easy ways by which artificial intelligence can transform the finance industry.
• Despite the disruptive innovations that have stemmed from it, it’s undeniable that more incremental and architectural innovations will crop up in financial AI in the coming years.
• Additionally, AI can handle high-volume transactions quickly and efficiently, allowing financial institutions to optimize their operations and provide better customer service.
• In the coming years, automation will forge new pathways in the industry and become a standard for personalized finance services.

They can employ well-known methods like Principal Components Analysis and Linear Discriminant Analysis for the latter . A single transaction can consist of hundreds of data points, which is why financial firms are considered to be sitting on data troves. These AI-enabled toolkits look for outliers that demonstrate data bias and remove them from the data flow. It’s also helpful to generate synthetic data by analysing clustered data points to increase the efficiency of the models involved.