We have entered an era fueled by AI, thanks to the declining costs of data storage and processing, improved accessibility and connectivity for everyone, and swift advancements in AI technologies. Machines powered by Artificial Intelligence are customizing recommendations for digital content based on individual tastes and preferences. They are creating clothing lines for retailers and, notably, are starting to outperform experienced doctors in identifying indications of cancer.
Banking stands out as one of the three primary sectors poised to experience the most significant impact from artificial intelligence. A report from McKinsey suggests that the implementation of AI technologies has the potential to provide an additional value of up to $1trillion annually.
In this article we will explore meaningful perspectives on the importance of AI in the banking sector, utilizing Coforge’s examples and case studies. Additionally, we will examine the challenges and constraints faced during the integration of AI into banking processes. We will then outline a strategic path forward, providing a preview of upcoming trends in this dynamic landscape.
AI and Generative AI have the potential to deliver significant new value to banks. Banks across the board will likely see an economic boost, with the biggest winners being the corporate and retail sectors. While the initial focus of Generative AI pilots in banking has been on boosting productivity in response to economic pressures, the technology holds significant transformative potential. It could fundamentally alter how tasks are performed, customer engagement occurs, and even pave the way for entirely new business models within the financial sector.
The prevalence of fraudulent insurance claims poses a significant challenge for insurers, requiring them to dedicate considerable time and resources to meticulous claim validation. However, traditional methods are often costly and susceptible to vulnerabilities. Fortunately, the emergence of Artificial Intelligence (AI) offers a compelling solution. Leveraging its exceptional pattern recognition abilities, AI can significantly enhance claim processing, enabling:
Current customer service models in banking often struggle to meet the demand for timely, personalized, and accurate support. Everyday inquiries can lead to frustrating information gaps, long wait times, and inconsistent service quality. However, the advent of Generative AI presents a transformative opportunity to revolutionize banking customer service. By leveraging AI's capabilities, banks can guarantee seamless access to 24/7 support, regardless of time or location. This empowers customers to resolve issues and access information at their own convenience, significantly improving overall satisfaction and experience. Beyond accessibility, Generative AI unlocks a range of benefits:
AI's exceptional pattern recognition and anomaly detection capabilities enable it to differentiate between legitimate and fraudulent activity with remarkable accuracy. By analyzing vast datasets of financial transactions, AI algorithms can identify suspicious patterns, flag potential threats in real-time, and even automatically initiate countermeasures to block fraudulent activity before it can inflict damage. This proactive approach leads to a multitude of benefits:
Traditional transaction monitoring systems often struggle with a high rate of false positives, frustrating customers and delaying legitimate transactions. However, Artificial Intelligence (AI) offers a promising solution. By analyzing a customer's spending patterns over time and across multiple variables, AI algorithms can accurately identify truly suspicious activity with minimal disruption to genuine transactions. This leads to a two-fold benefit:
Mired in resource-draining manual processes, traditional credit risk profiling for loans, cards, and mortgages struggles with speed and accuracy. AI, however, offers a transformative solution. By analyzing vast digital data, AI programs automate risk assessments, leading to:
Traditional KYC and Due Diligence, often semi-manual and prone to missed periodic checks, lack real-time risk alerts for potentially problematic customers. AI presents a potent solution. By leveraging comprehensive KYC details and external information, AI can:
Implementing regulatory changes and comprehensively assessing risk factors in banking has been a time-consuming and manual process, often hampered by data compliance issues. However, Artificial Intelligence (AI) emerges as a powerful solution, empowering compliance analysts to navigate the complex regulatory landscape with greater efficiency and accuracy.
We assist our clients in a diverse range of Financial and banking sectors, including global banks, marquee central banks, government agencies, fintech companies, cards and payment providers, and global investment managers. 3 decades of experience, 7000 SMEs, the right tech capabilities and partnerships have made this the largest and fastest growing vertical for us. With assets on platform, solutions, and services worth $1.5Tn, 200+ managed platform services, we support 200+ financial institutions, delivering 50K+ complex programs.
The latest addition in this journey is our own platform, Quasar AI, designed to build Enterprise AI capabilities to help our clients in realizing the use cases listed above. The Quasar suite encompasses a range of advanced AI solutions tailored to address diverse data processing and analysis needs. These sophisticated offerings are designed to cater to various aspects of data analytics, utilizing cutting-edge technology to deliver valuable insights. Below, we elaborate on each component of the Quasar suite with their industrial applications.
Our proprietary Responsible AI Engine and framework plays a pivotal role in identifying and explaining biases within datasets. Quasar Responsible AI uncovers potential risks and compliance challenges, providing options to govern, mitigate, and remediate third-party risks where necessary. In a world where anti-discrimination and privacy laws are becoming increasingly stringent, our Quasar Responsible AI Platform provides a robust framework for ethical AI integration. Here are some of our Responsible AI solutions:
We empower clients to embrace AI by clarifying their needs and guiding them towards the perfect solution. Our proven approach ensures a product that matches their business goals, closes performance gaps, and leverages data effectively. Here are a few solutions we provided to the clients:
Case study 1:
Problem statement: A pressing issue in the financial sector relates to the rise in anti-money laundering (AML) activities, coinciding with a decline in detection rates. Traditional approaches involve utilizing customer data (KYC details) and behavior patterns for clustering, yet this method encounters limitations. Customers are intricate entities, necessitating ongoing surveillance for unusual behavior, rendering transaction rules inadequate.
Solution: We meticulously analyzed vast amounts of banking transactions, identifying distinct patterns and behaviors. This led to the creation of customer clusters, each akin to a financial fingerprint. Within these clusters, each customer serves as a prototype, representing the typical transaction profile. Suspicion now falls on individuals who fall outside the established norms, either by failing to fit any cluster, exhibiting misalignment within their assigned group, or significantly deviating from the expected prototype behavior. This targeted approach pinpoints potentially fraudulent or unusual activity with unprecedented precision, streamlining investigations and safeguarding the financial security.
Result: This in-depth examination of customer behavior yields several benefits, including enhanced fraud management, improved fraud detection rates, a noteworthy reduction in false positives, more efficient workload management, and a decrease in manual intervention. This approach equips financial institutions with a more robust and adaptive framework for combatting AML activities effectively.
Case study 2:
Problem statement: A prominent global bank encountered a significant obstacle in its risk management process. The bank relied on annual supplier-issued reports, validated by external auditors, to define and assess supplier controls. These reports were crucial for their supplier risk management framework. However, the manual analysis of these reports, comparing them year-over year to identify control changes, proved cumbersome and error-prone, hindering efficient risk assessment. To address this challenge, the bank sought a more streamlined solution.
Solution: We leveraged machine learning techniques to efficiently compare the reports and identify changes. The process included sentence extraction and preprocessing removing irrelevant characters, applying text normalization techniques, and tokenization. An LLM model was employed to measure the semantic similarity between sentences from corresponding reports. Sentences that match completely are removed and non-matching sentences are highlighted in the source file. A summary report is generated, listing all non-matching sentences and their locations within the respective PDFs. This report provides a concise overview of potential control modifications.
Result: This ML solution presents a transformative paradigm shift, empowering a faster, more accurate, and transparent risk assessment process. Automated processes seamlessly handle report comparison, dramatically reducing time and effort invested, leading to enhanced efficiency and high accuracy.
Case study 3:
Problem statement: A leading National Commercial bank faced a challenge to develop a robust system capable of accurately extracting relevant information from cheques, ensuring seamless integration into financial processes. This involves addressing issues related to handwriting recognition, varied formats, and potential errors, with the goal of enhancing efficiency and accuracy in financial transactions.
Solution: Our innovative approach to tackling the extraction of crucial information from cheques involved leveraging Yolo for signature extraction. Yolo, known for its real-time object detection capabilities, provides a robust foundation for identifying and isolating signatures on cheques efficiently. Building on this, we implemented a trained spacy model to carry out the extraction of key details such as the payee’s name, serial number, account number, amount, and accuracy. The Spacey model, honed through meticulous training, demonstrated remarkable accuracy in discerning and isolating specific elements within the cheque. This dual-model strategy combined the strength of Yolo for signature identification with the nuanced understanding of Spacey for extracting textual information. The synergy between these models ensured a comprehensive retrieval of relevant data from the diverse formats that cheques may present.
Result: Our system not only streamlines the extraction process but also enhances accuracy minimizing errors in crucial financial data. By integrating YOLO and Spacey, we have developed sophisticated solution that stands at the forefront of technology, addressing the intricacies of cheque processing for improved efficiency in financial workflows.
AI is rapidly reshaping the landscape of banking and finance and will continue to do so. Banks and financial institutions are leveraging its power to streamline operations through automation, personalize customer interactions with intelligent solutions, and drive growth through innovative data-driven insights. Several leading banks such as JPMorgan Chase, Mastercard, Bank of America have used AI and ML techniques for processes like automated contract reviews, identifying fraudulent transactions, and providing personalized recommendations.
Few of the potential use cases for banking are:
By actively embracing AI and continuously exploring its potential across diverse applications, banks and financial institutions can confidently navigate the evolving landscape and solidify their competitive edge. Coforge, recognized for its unwavering commitment to client success, consistently strives to be a trusted partner in this transformative journey, providing the expertise and resources necessary for the clients to harness the full potential of AI and achieve sustainable growth.
https://www.coforge.com/industries/banking-and-financial-services
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