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AI in financial sector

Reforming Banking with Artificial Intelligence

How AI is applied in many use-cases of banking

 

Artificial Intelligence (AI) is leaving no stone unturned: many industries are leveraging the power of AI to deliver quality service and value to their customers, and the financial sector has been one of them, as traditional banking methods are replaced with cutting-edge intelligent systems.

0_SR04rfjbldSP93rQPhoto by Andrea De Santis on Unsplash

Over the last three decades, the development of AI has accelerated and has been applied to a wide range of domains and subjects, as AI assists in obtaining clear and useful insights by analyzing data. Unlike traditional systems, AI does not depend on any pre-defined functions; instead, it depends on the data to find patterns based on previous examples and generate a specific algorithm accordingly, and AI can handle complex data as well, such as images, videos, and text.

Artificial intelligence is revolutionizing the way how the banking industry operates, enhancing the services that customers would receive. The impact of AI in banking is visible as the market value of AI utilized in banking is expected to top $60 billion by the end of this decade. With the effect of the pandemic, it is of utmost importance for banking organizations to adopt AI to understand the new normal.

In this article, we’ll have a look at how the banking industry leverages the power of Artificial Intelligence to its benefit.

AI in Fraud Detection

$3.5 trillion is lost by businesses each year due to fraud, as per the Association of Certified Fraud Examiners. The magnitude displays how flawed the traditional human-written rules are in detecting fraudulent transactions. And, banks undergo millions of transactions each day: transactions based on various monetary values, using various currencies, various types of individuals, and from different time zones and locations. Processing every transaction to identify whether one is fraudulent or not, using traditional methods can be a Herculean task, and would cost a lot of money and human resources. Thanks to AI, the process is heavily simplified, and this task has been one of the earliest use of AI in banking.

Machine Learning (ML), an important branch of AI, is utilized to process millions of transactions and can be used to predict whether a transaction is legitimate or not. Supervised and unsupervised techniques are used to detect fraudulent transactions.

Supervised ML techniques require labels, where the examples of fraud and non-fraud transactions are labeled, and machine learning models are trained to classify whether a transaction is fraudulent or not. This eliminates the need of designing sophisticated mathematical algorithms and rules manually, as the machine learning models would generate their algorithm and rules based on the examples.

Considering real-world situations, analyzing and labeling each transaction would be expensive and can be erroneous (when multiple labels are involved). In such scenarios, unsupervised machine learning techniques are used, where the algorithm analyzes every transaction, uncovers hidden patterns, and groups the transactions based on their similarities.

There are many successful AI integrations in combating fraudulent transactions: Dankse Bank, a Nordic universal bank, has achieved an 80% score in accuracy in detecting fraudulent transactions using machine learning, easily outperforming their previous human-written rules engine, which had an accuracy score of 40%; and Capgemini has stated that AI and data analytics can speed of the fraud investigation by 70% and improve the accuracy in detecting fraudulent transactions by 90%. This clearly shows the impact of how Artificial Intelligence can power banks in efficiently and accurately detecting fraudulent transactions.

 

AI in Privacy

Privacy is the most crucial component of any banking institution, as they have access to all the personal, financial, and transactional details of their customers. With the ever-growing threats of privacy breaches and data leaks, banks face financial, legal, and reputational risks. And, this would prevent the sharing of data, even within the internal teams of the banks. However, Artificial Intelligence can provide a groundbreaking solution to solve this problem: generating synthetic data.

Synthetic data is artificially generated data that mimics real-world data. By using a few well-labeled data and AI, entirely new and statistically accurate synthetic data, which closely represents the real data, can be generated. This synthetic data can be used for any purpose, and the sensitive information is not exposed.

Synthetic data facilitates sharing of data within the teams in the banks, as privacy is protected and access to the real data remains untouched. This also enables banking institutions to collaborate with external organizations and partners, for building data solutions. Banking institutions can utilize synthetic data to create new revenue streams as well, as the synthetic data can be sold for a hefty sum without breaching any regulations. Synthetic data also accelerates the model-building process in machine learning, as synthetic data can be used for training and validating models, without exposing sensitive information to the models. Synthetic data can also be used to simulate data representing abnormal situations, such as fraudulent transactions, as in real-world situations, the number of fraudulent transactions is significantly less compared to legitimate transactions.

Popular companies are leveraging the power of synthetic data for various use cases: American Express is generating synthetic data for financial transactions to predict fraudulent transactions, and a Fortune 100 bank company is saving millions of dollars by collaborating with external vendors by building synthetic data lakes.

Generating synthetic data with open-sourced libraries is quite straightforward and you can refer to this article, which provides a step-by-step explanation of generating synthetic data using GANs.

 

AI in Customer Engagement

Conversational AI is revolutionizing the way how customers want to access services, as it provides a unique and personal method for customers to ask what they want in plain language, just as they would converse with a real human, but with a bot rather than using search engines. Tech giants such as Google and Meta are experimenting with Conversational AI and have made huge strides, as is evident in their groundbreaking research LaMDA and BlenderBot. The banking industry is highly benefiting from utilizing conversational AI.

Chatbots, an important part of conversational AI, can be found in major banking institutions, as they can handle the common and repetitive tasks that many customers would require, such as transferring money, accessing statements, balance inquiries, etc. Chat assistants can be made available in both text-based and voice-based and is always available 24x7 for the customers.

The use of chat assistants can significantly save costs and assist in reducing the pressure faced by calling centers and banking offices while providing customers with a human-like experience in accessing services. Chat assistants reduce the risk of data leakage as well, as there is no requirement for the customer to divulge sensitive information to the chat assistant; and conversational agents do not require complex infrastructure and servers, which enables even smaller banking institutions to integrate chatbots into their services.

The benefits of integrating conversational AI in banking services are visible as more than 40% of banking customers prefer chatbots other than visiting the branch, using the website, etc. Chat assistants can be invaluable in saving costs as it is projected that $7.3 billion will be saved by banks that utilize chat assistants, and chatbots also save 4 minutes for each inquiry. Leading banking institutions, such as Bank of America’s Erica, are utilizing and promoting chat assistants for customers to access banking services.

 

AI in Lending

One of the major services provided by banks is loans. Many banking institutions require extensive information about the individuals or organizations that require loans and also face risks of losing money when their customers become delinquent or charged off. And generally, traditional lending methods tend to discriminate against customers based on their gender, religion, race, etc.

Artificial Intelligence and Machine Learning enable financial institutions to assess their potential customers based on their transactions and provide an unbiased credit score focused only on their digital footprint. It is an extensive analysis of several data sources, such as credit bureau data, social network presence, payment activities, etc. ML techniques can also be used to predict whether a customer would default on the loan repayment or not even before the intended loan repayment dates, and this can enable the banks to identify at-risk customers and provide loan restructuring services.

The current economical situation reiterates the importance of AI for lending. Inflation has affected multiple countries, businesses, and individuals, and their ability to make payments for the loans, which has resulted in countries defaulting on international loans and bankrupting organizations and individuals. The pandemic COVID-19 has also affected international markets significantly and has affected many individuals on a global scale. Using traditional methods to evaluate the customers with external variables is futile, and AI can extensively assist in considering the effect of multiple variables for banking organizations to make lending decisions.

AI techniques significantly reduce the cost, time consumption, and proneness to error, that is required to analyze and process potential customers, provide loans, and identify at-risk customers, who might fail to make their payments on time. There are real-world examples of how successfully AI is reshaping the lending industry: Lenddo has built a credit scoring system, based on the digital footprint of its customerstech giant Amazon uses machine learning to offer loans to small businesses based on various criteria, and the use of AI in loan assessments is projected to push digital lending and banking institutions past $100 billion by 2023.

 

AI in Understanding Documents

Financial documents are a cornerstone in any banking institution and going through countless financial documents (from opening a bank account to applying for a credit card, and transferring funds), and analyzing and extracting essential information manually is tiring and repetitive. Therefore, many banking institutions utilize the power of AI to digitize, classify, and extract data from documents. AI-powered systems take just a fraction of the time needed for a human to process documents.

AI and Optical Character Recognition (OCR) combine to create Intelligent Document Processing (IDP) systems, where AI helps in identifying important data points in documents, extracting their contextual meanings, and converting unstructured data to structured information. This enables IDP systems to process large volumes of documents, such as account opening/closing forms, withdrawal/deposit slips, cheques, financial statements, etc., and extract the details while saving time, and cost and also being less error-prone. IDP systems can also classify different types of documents and aggregate data from them as multiple documents are required by customers when applying for loans, mortgages, credit cards, etc. IDP systems enable banks to have a faster Know Your Customer (KYC) process compared to manual methods, by accurately and efficiently processing customers’ identification documents and financial statements.

Utilizing AI to process documents can save banking organizations 30%-40% of the hours typically spent in manual processes. AI and IDP systems can also assist banks in improving compliance by having a clear KYC process, as banks have lost $450 billion in penalties due to non-compliance. Popular banking institutions such as HSBC and Citibank, are integrating AI to process millions of documents, such as invoices and insurance documents, rather than utilizing human labor. And, many organizations have open-sourced their cutting-edge research on document understanding, such as LayoutLM by Microsoft, which enables any banking institution to utilize AI to create powerful IDP systems without building them from scratch.

Final Words

Artificial Intelligence is inevitable. Due to the global digitization and the after-effects of the pandemic, AI is invading almost all industrial sectors, and banking is no exception. AI has enhanced and revolutionized how banks interact with their customers, and process and analyze massive volumes of data consistently and efficiently. As discussed in the above sections, reducing the time taken for manual labor, and conserving costs are two major advantages that banking institutions gain by incorporating AI in their products and services. AI is set to power the banking industry for the next decades and provide a unique experience for both customers, assisting financial institutions to grow closer to their customers and helping in reaching the unbanked population.