Threat modeling is a crucial component of cybersecurity, particularly for industries such as banking, where the security of financial data is paramount. Traditional threat modeling approaches require expert intervention and manual effort, often leading to inefficiencies and human error. The advent of Large Language Models (LLMs) offers a promising avenue for automating these processes, enhancing both efficiency and efficacy. However, this transition is not straightforward due to three main challenges: (1) the lack of publicly available, domain-specific datasets, (2) the need for tailored models to handle complex banking system architectures, and (3) the requirement for real-time, adaptive mitigation strategies that align with compliance standards like NIST 800-53. In this paper, we introduce ThreatModeling-LLM, a novel and adaptable framework that automates threat modeling for banking systems using LLMs. ThreatModeling-LLM operates in three stages: 1) dataset creation, 2) prompt engineering and 3) model fine-tuning. We first generate a benchmark dataset using Microsoft Threat Modeling Tool (TMT). Then, we apply Chain of Thought (CoT) and Optimization by PROmpting (OPRO) on the pre-trained LLMs to optimize the initial prompt. Lastly, we fine-tune the LLM using Low-Rank Adaptation (LoRA) based on the benchmark dataset and the optimized prompt to improve the threat identification and mitigation generation capabilities of pre-trained LLMs.

Banking, financial institutions & customers have been facing fraud for a very long time, in fact ever since the financial industry was created. The chances of fraud being attempted are almost guaranteed wherever money and/or private data are present. As the use of digitization and use of technology increases, it also increases the ways and means for fraudsters to leverage the same technology to commit fraud. Fraud detection identifies an actual or expected fraud that has or may take place using advanced technologies like AI, OCR, and ML to identify potential threats, mitigate risks, and prevent their recurrence. Banks, financial institutions & any other organization that deals with money, finance, or any other financial instrument need to implement strict measures, systems, and processes in place to detect fraud at an early stage or if possible before it takes place.

In recent years, we can witness that artificial intelligence is becoming a need in every domain of the industry, and AI's different domains, such as computer vision, natural language processing, and predictive modelling, are helping humans solve their use cases and problems more effectively and without the intervention of the humans. We can also enjoy the intervention of AI in our daily life, and humans are becoming more curious about this intervention. Banking sectors are also positively affected by the intervention of AI. In this article, we will cover some of the critical use cases of AI in the banking sector that is helping humans advance the banking sector. This sector implies AI-enabled models to assist the customer during onboarding.

As the opinion rapporteur for the Artificial Intelligence Act in the Committee on Culture and Education (CULT), I will present a proposal for amending the Artificial Intelligence Act in March. The draft focuses on several key areas of artificial intelligence (AI), such as high-risk AI in education, high-risk AI requirements and obligations, AI and fundamental rights as well as prohibited practices and transparency obligations. The regulation is aiming to create a legal framework that prevents discrimination and prohibits practices that violate fundamental rights or endanger our safety or health. One of the most problematic areas is the use of remote biometric identification systems in public space. Unfortunately, the use of such systems has increased rapidly, especially by governments and companies to monitor places of gathering, for example.

For instance, Philadelphia-based fintech firm cred.ai, The card was licenced by payments network Visa and issued by Wilmington Savings Fund Society, FSB. The credit optimizer tool uses an AI algorithm to improve the user's debt-to-credit ratio, which accounts for up to 30% of a FICO score that evaluates a person's creditworthiness in the US. Apple, too, uses AI to determine a user's credit limit on the Apple Card. Closer home, Gurugram-based fintech firm OneBanc has developed a card to connect various banking systems.

Some people think the current banking system will stay stable and unchanged for a relatively long time because of how well-designed and mature it is, but the truth is cutting-edge technologies such as AI should be deployed as a supporting tool to help make banks competitive and relevant for customers, especially in the areas of fraud prevention, risk control, and customer communication. The time and energy saved can be used in finding high-quality investment projects, maintaining customer relations, and performing internal and external control, which are obviously more valuable than repetitive tasks. Getting AI processes to scale is relatively easier with all the algorithms and programs that are known. For example, the compliance costs of small banks account for an average of 7% of their non-interest expenses, most of which is the salary of employees, followed by data processing, accounting, legal and consulting fees. If banks use AI to cover these processes, the cost can be reduced.

An artificial intelligence-powered platform designed to enhance traditional mobile banking with seamless crypto integration is about to launch v2 of its mobile application, describing it as an all-in-one crypto-financial solution. BlockBank, which describes itself as a platform designed for professional traders and new retail market participants, reported that the new version of its application is a significant advancement compared to what has been offered in the space so far. The application is said to consist of four main components: a centralized custodial wallet, a non-custodial Web 3.0 wallet, banking and an AI-powered robo-advisor. The team emphasized that users won't have to sacrifice security, privacy or decentralization when using its application. One of the measures taken to ensure a high level of security is BlockBank's partnership with Shield Finance, a multichain decentralized finance insurance aggregator, and Bridge Mutual, a platform that integrates a DeFi risk coverage application into its system.

During this pandemic, we are becoming aware of the strong need for a massive digital transformation that allows the banking system to consciously and responsibly take care of customers, account holders, stockholders and other investing entities. Computerizing the processes in management and control has already passed. It's not enough in 2020 when a considerable part of savings and ordinary transactions are being acquired and processed by the new FinTech platforms and hybrid banks, often without holding a banking license. Some of the most powerful digital-only banks are Revolut, Chime, N26, Up, Monzo, Nubank and Starling. Many banks are exploiting digital tools, which is satisfying, but not enough until they move away from customer/user management as it happens in ordinary banks, face to face and hand to hand, and in private, corporate and retail sectors.

At a time like this, the banking sector is trying its hand, leg and even head to give a head-start to the AI developments. The financial services industry is appealing to enter AI market to avail the luxury of accurate data and investment. The development assists banks with better customer service, fraud detection, reduction of managing cost and easy decision-making through AI analysis. Customers have expectations that can't be turned down. Expectations to get work done faster and with zero error. The only by-standing solution is the utilisation of AI in the everyday banking sector.

The advent of technologies such as artificial intelligence (AI), machine learning and the internet of things (IoT) have transformed the way in which the world operates. Customers now demand personalised experiences and firms that haven't digitised their businesses must transform in order to compete in this new world. One industry which has found it more difficult to adapt is banking, as many banks are saddled with rigid and core legacy application systems. Large banks and financial institutions continue to remain slow to adopt these emerging technologies which is preventing them from delivering an optimal customer experience, driving growth and keeping themselves relevant in a dynamic market. Banks must modernise their legacy banking systems in order to compete in this evolving marketplace.