Fraudulent activities in the financial industry cause significant losses every year, making it imperative for financial institutions to have an efficient and accurate system in place to detect and prevent such activities. “Collectively, the bank frauds have resulted in losing at least ₹100 crores every day over the past 7 years in India“, reported RBI. It is a never-ending arms race between fraud detection systems and those seeking to commit fraud because fraudsters, too, are constantly evolving and refining their techniques. While classical computing-based methods for identifying fraud have been useful in the past, they have some drawbacks, including difficulty in quickly handling massive amounts of data while also keeping up with the financial market’s constant change. When it comes to speed, precision, and scalability, quantum computing significantly outperforms traditional computing as a solution. In this blog, we will explore how quantum computing is revolutionizing fraud detection in the financial industry and how it has the potential to greatly improve the efficiency and accuracy of fraud detection systems.
What is Fraud Detection?
The process of identifying and stopping fraudulent, unrecognized, or unauthorized actions/ activities that lead to the loss of money or property is known as fraud detection. It includes identifying and ceasing criminal activities like money laundering, credit card fraud, insurance fraud, and identity theft. Fraud detection aims to identify and stop fraudulent activity while lowering the possibility of false positives and limiting the impact on legal transactions quickly and effectively. It also protects financial institutions, organizations, and individuals from such attacks and losses, and it is considered a crucial part of risk management. It is usually accomplished by combining human analysis with automated systems that employ algorithms and machine learning approaches to recognize and flag questionable activities.
How does Fraud Detection work in Finance Industry?
In the financial sector, fraud detection often combines manual and automated techniques to detect abnormal or questionable transactions or behaviour. The following steps are typically included in the process:
- Data Collection: Fraud detection systems gather data from various sources, such as transaction records, customer profiles, and other relevant information.
- Data Analysis: The collected data is analyzed to identify patterns, correlations, and anomalies that may indicate fraudulent activity. This process often involves the use of machine learning algorithms, statistical analysis, and other data analytics techniques.
- Rule-based Detection: The system may use predefined rules or algorithms to identify transactions that match known fraud patterns or red flag criteria.
- Risk Scoring: Transactions are assigned a risk score based on the results of the data analysis and rule-based detection, with higher-risk transactions flagged for further investigation.
- Human Review: The flagged transactions are reviewed by human analysts to determine if they are indeed fraudulent. This step helps to minimize false positive detections.
- Response and Investigation: If a transaction is determined to be fraudulent, the financial institution takes the appropriate action to prevent any further loss, such as reversing the transaction or contacting law enforcement.
This process is repeated continuously to ensure the system stays up-to-date and can detect evolving fraudulent activities.
Another approach used by classical computing is data mining and machine learning techniques, where algorithms are trained on historical data to identify patterns and correlations that may indicate fraud. These algorithms can adapt to new and changing financial data and improve their accuracy over time. Classical computing also utilizes artificial intelligence (AI) and natural language processing (NLP) to analyze unstructured data, such as customer feedback, to identify potential fraud.
Why cannot Classical Computers keep up?
Below are a few points that make the traditional approach less desirable as compared to the much more powerful quantum approach:
- Traditional rule-based fraud detection methods require complex and numerous rules to be defined, which must be continuously updated to keep up with the evolving techniques of fraudsters. Also, these rules only identify known patterns of fraudulent behaviour and cannot detect new, unknown patterns. This makes it difficult to identify and flag suspicious behaviour in a timely and accurate manner, leading to high false positive rates and missed fraud cases.
- With large amounts of data, the traditional approach also becomes computationally expensive and inefficient, requiring a significant amount of time and resources. Additionally, classical computers require a considerable amount of memory and computational power to perform data processing and analysis, making them unsuitable for real-time fraud detection.
- The algorithms that are used to detect anomalies cannot handle the complexity of high-dimensional data, such as transactional data, due to the so-called “curse of dimensionality”. This makes it difficult to capture the intricate and dynamic relationships between different financial variables and to identify fraudulent behaviour that may be hidden within these relationships.
Why Quantum Computing?
Most of you must be aware by now of the incredible capabilities offered by quantum computing. Quantum computers have a significant advantage in terms of speed, accuracy, and scalability. It will be feasible to examine market data in real-time and make prompt, well-informed judgements thanks to quantum algorithms, which can handle enormous volumes of financial data far more quickly than classical algorithms. Additionally, quantum computing’s ability to handle complex financial models and market dynamics makes it well-suited to solving the challenges of fraud detection.
In the case of fraud detection, quantum computers can be used to analyze massive volumes of financial data simultaneously and find connections and abnormalities that might point to fraudulent behaviour. Quantum computers’ increased processing capacity may also stimulate the adoption of more complex simulations and algorithms, leading to better overall precision. For instance, by analyzing massive volumes of financial data and transaction history to spot correlations and abnormalities in real time, quantum computers can be utilized to provide real-time risk scoring. This can help financial institutions identify and respond to potential fraud much more quickly and effectively, reducing the impact of fraud on their customers and the financial industry as a whole.
Furthermore, quantum computers can also be used to perform complex simulations and scenario analysis, allowing financial institutions to test and improve their fraud detection systems in a controlled environment before deploying them in real-world situations.
Where and How Quantum Impacts?
- Unstructured Search: Large volumes of financial data can be processed quickly by quantum computers through an unstructured search to find abnormalities and connections that might point to fraud. As a result, financial organizations have a higher probability to spot possible fraud more precisely and rapidly.
- Optimization Problems: Fraud detection often involves finding the optimal balance between false positives and false negatives. Quantum computers can solve optimization problems faster and more accurately than classical computers with the help of superposition and entanglement. This would help financial institutions fine-tune their fraud detection systems to reduce false alarms and improve accuracy.
- Machine Learning: Quantum computers can be used to train more sophisticated machine learning models that can identify correlations and anomalies in financial data with higher accuracy. This can help financial institutions stay ahead of evolving fraud threats.
- Cryptography: Quantum computers can also be used to perform complex cryptography algorithms, which can enhance the security of financial transactions and communication, reducing the risk of fraud.
By leveraging these certain types of calculations, quantum computers can offer significant advantages for fraud detection in the finance industry. These advantages include faster processing, improved accuracy, and the ability to handle more complex algorithms and simulations, which can help financial institutions stay ahead of evolving fraud threats.
Who are Exploring and Investing in Quantum Computing?
There are multiple organizations that have already begun researching and working on quantum computing in the finance sector, and there are a few success stories as well. Prominent names include JPMorgan Chase, Wells Fargo, Goldman Sachs, Barclays, Citi Group, Mastercard, HSBC, NatWest, Crédit Agricole, Royal Bank of Canada, BNP Paribas, and many more.
This leads us to believe that despite being in its nascent stage, quantum computing is quite capable, as it is already accepting challenges and clearing obstacles. In the coming few years, as the technologies advance, it will definitely claim its throne and emerge as a significant technological revolution.
Special thanks to Vittal Setty for his help and encouragement.