A Financial Analysis and Fraud Detection Tool FraudGuardian is a project focused on developing an advanced financial analysis and fraud detection tool. The tool aims to identify potential fraudulent activities within financial institutions through data analysis, machine learning, and visualization techniques.
In the modern-day intricate and hastily evolving economic landscape, correct analysis, risk evaluation, and fraud detection have come to be essential. Financial institutions, corporations, and people depend on sturdy strategies to shield their property and make well-informed decisions. Welcome to the Financial Analysis and Fraud Detection AI Project – a groundbreaking initiative that harnesses the energy of Artificial Intelligence (AI) to revolutionize monetary protection and decision-making tactics.
The solution is relevant to the theme because we saw that in many financial aid institutions, finances are often given wrongfully to those who do not deserve it and to those who do not even exist. Financial aid institution (NSFAS) is helping a lot student in need in order to further their studies and to be successful in life. It is sad that most deserving students are unable to get funding because of greedy people who try to fraud the NSFAS. Therefore, we have developed an AI model called Financial analysis and Fraud detection that uses the day to day data to predict potential fraudulent activities within Institution sectors. Financial aid organizations provide funding for students who are in need in order to further their studies. Our model uses day-to-day data to provide the accuracy of these occurrences. The use of a heuristic narrows down the search for a solution and eliminates the wrong option. Our model offers institutions security and accuracy when it comes to finances.
In the realm of finance, characterized by swift technological transactions, the challenges of accurate financial analysis and the persistent specter of fraud pose significant hurdles. Traditional methods of financial analysis struggle to cope with the complexities of modern financial systems, while evolving fraudulent tactics demand a proactive response. For the past years, most financial aid institution have been paying double the funds. This is due to non-existing students and underserving students.
First case is that the university has 100 000 students who are in need of financial aid (NSFAS) funding. Instead of sending the correct list to financial aid (NSFAS), the university sends 200 000. This means the NSFAS ends up using double the funds monthly paying for non-existing students or undeserving students. There is a high rate of crime in the financial aid institutions. For example, NSFAS found that it lost R5bn by funding over 40 000 undeserving applicants between 2018 and 2021 (NSFAS Annual Report).
Those funds have not been recovered today. The problems are perpetrators using fake identity numbers and the system not picking the funding is for the undeserving applicants. This form of financial fraud can be likened to an electronic heist, with perpetrators utilizing tactics such as cloning or skimming of students' identity numbers. In some instances, fraudsters go to the extent of creating fake identity numbers to apply for financial assistance, effectively robbing these financial aid institutions. For uninformed institutions, it is difficult to detect. Fraudsters in our problem is the students using fake ids or university management sending the wrong list deliberately. As we know that funding comes from the NSFAS to the university and the university pay students.
In addition, another critical problem is slow responsiveness when it comes to fraudulent activities. For example if people collect funds from the institution using fake identity numbers, the institution may take years without knowing anything due to human error or wrongfully inputting the data. Financial Agencies are drowning in data, collecting terabytes of it each day. AI and ML’s primary use is data management, specifically making large amounts of data searchable, filterable and retrievable in real-time.
This project transcends conventional methods by integrating cutting-edge technology into financial workflows. Its mission is to enhance financial analyses' accuracy and optimize real-time detection of potentially fraudulent activities. By harmonizing AI algorithms with financial intricacies, this project offers an all-encompassing solution that empowers organizations and individuals to achieve financial stability and growth.
As you embark on this exploration of the synergy between AI and finance, you will witness the transformative power of algorithms decoding complicated economic trends, quantifying and managing risks, and safeguarding financial prosperity. This adventure will unveil a future wherein AI reshapes the contours of the economic panorama – a realm where economic operations are streamlined, selection-making is optimized, and safety is fortified through clever automation.
The Financial Analysis and Fraud Detection AI Project ushers in a brand new generation of monetary operations, strengthened with the aid of the prowess of AI. Join us as we delve into this realm of innovation, wherein the era's precision meets finance's complexity, ensuing in multiplied accuracy, informed choices, and fortified economic integrity for all stakeholders.
How will this AI benefit the communities, which are the financial aid institutions?
Benefits of Implementing Financial Analysis and Fraud Detection AI in Financial InstitutionsWhen we look at the high crime or fraud rate in our community, it is clear that we still lack when it comes to responsiveness and alerting institutions when in fraud activities, so our AI model will help our community reduce the number of crimes.
Improved Financial Aid Distribution:
Ensures that financial aid goes to deserving students who are genuinely in need, helping them pursue their education without unnecessary delays or financial hardships. This model is going to reduce the chances of financial aid funds being wasted on ineligible or fraudulent recipients, ultimately making the system more equitable.
Enhanced Financial Security:
Protects the institution's (NSFAS) financial resources by proactively identifying and preventing fraudulent activities, thus safeguarding the institution's funds. Increases the institution's financial stability, which can lead to better long-term planning and allocation of resources. This will be beneficial and advantageous to eligible students.
Detects fraudulent activities in real-time or near real time, enabling universities to take immediate action, recover funds, and prevent further losses. Avoids situations where fraudulent activities go undetected for extended periods, minimizing the overall financial impact.
Demonstrates the institution's commitment to financial transparency and responsible stewardship of funds, which can enhance the institution's reputation and build trust within the community. In the recent years, the stats showed that more people would rather choose to apply for other bursaries than NSFAS because most lost the trust in it. You apply for NSFAS it rejects you and it takes an identity number, which does not exist and funds it. Our model is going to help to attracts more qualified and deserving students who feel confident that the financial aid system is fair and secure.
Reduces the financial losses associated with fraud, potentially saving universities significant sums of money over time.
How Machine Learning Is Used in Financial Analysis and Fraud Detection.
We have trained our model to help analyze, and flag the personal details that are mostly used to commit this kind of fraud, which is mostly the identity numbers. By identifying identity numbers originality, Our AI model can flag suspicious applications or transactions in real time. Our AI model can learn and adapt to evolving fraud patterns. It can recognize new trends in fraudulent identity number usage, helping the financial institution (NSFAS) to stay ahead of fraudsters' tactics
The machine learning algorithms we used are Supervised and Unsupervised
For the Supervised algorithm, we used a set of labeled data. The dataset includes names, surnames, identity numbers and application numbers etc.
First, we created a training dataset, with this dataset the model can analyze, create and spot the relationship between the factors and predict things. The major thing it uses is comparison. Our model is going to compare the data received from the university with the stored data from the student’s application. When students apply for financial aid, the institution store their data.
Therefore, our model is going to compare the two set of data to check if whether the student does belong to the university. That is financial aid (NSFAS) vs Student. The second one is comparing the universities sets of application list and we are going to store the Home Affairs datasets in our model so that it be able to check for the originality of the identity numbers. That is financial aid (NSFAS) vs the university. Supervised Machine Learning Techniques we used are
Classification:
Regression:
Feature Selection: Choose features that are strongly correlated with aid amounts, such as family income, number of dependents, academic performance, etc.
Time series analysis is a crucial technique in Financial Analysis and Fraud Detection. It helps organizations extract
valuable insights from historical financial data, predict trends, and identify potential fraud. Here's how it is applied:We harnessed time series data as a valuable resource because it allows us to examine patterns and trends over time, which is crucial in detecting fraudulent behavior that may evolve or exhibit periodicity To effectively manipulate and process the time series data in our model, we harnessed the capabilities of the Pandas package. With Pandas, we were able to perform various data operations, including creating date ranges, reassembling data, and altering frequencies. For instance, we could identify patterns where, at the end of every 4th month or period, which is application or registration cycle for the universities, there was a notable uptick in fraudulent activities.
Time series data proved invaluable in our predictive endeavors because it inherently encapsulates three critical components:
Trends: Time series data analysis enabled us to identify and comprehend underlying trends in fraudulent activities. By analyzing historical patterns, we could discern whether fraudulent occurrences were on the rise or decline.
Seasonality: Seasonal patterns within the time series data provided essential insights into recurring fraudulent trends. Recognizing these seasonal variations allowed us to make predictions that are more accurate.
Heteroscedasticity: This concept, denoting variance from the mean, played a pivotal role in our model. It allowed us to account for deviations from the expected, helping us identify unusual and potentially fraudulent data points.
Artificial Intelligence is beneficial for solving complex problems due to its efficient methods of solving. In this case, we going to use it for decreasing the rate of fraud. Therefore, we going to use Artificial intelligence to improve the financial security of NSFAS.
The implementation of advanced solution techniques is paramount in reaching correct monetary analysis and proactive fraud detection. Leveraging a combination of information-driven methodologies and present-day technology, establishments can improve their monetary operations and mitigate risks efficaciously. Here is an overview of key solution techniques applied within the area of Financial Analysis and Fraud Detection:
Machine Learning Algorithms: Employing a wide variety of system-studying algorithms, consisting of decision bushes, random forests, guide vector machines, and neural networks, institutions can analyze historical monetary facts to hit upon patterns, anomalies, and ability fraud activities. These algorithms analyze statistics, adapt to emerging tendencies, and decorate predictive accuracy.
Anomaly Detection Models: Anomaly detection models are educated to pick out uncommon behaviors or transactions that deviate from anticipated patterns. These fashions can help establishments flag ability fraudulent activities in actual time, ensuring quick intervention .
Predictive Analytics: By utilizing predictive fashions, establishments can forecast future marketplace developments, asset fees, and financial situations. These insights resource in developing knowledgeable investment techniques and hazard control plans.
Natural Language Processing (NLP):NLP strategies examine text-based facts, which include financial information articles and reviews, to extract sentiment and insights that have an effect on market tendencies. This facts publications investment decisions.
NLP is a branch of computer science that deals with teaching computers to understand human language, as humans actually use it.
NLP APPLICATIONSHuman language is filled with ambiguities that make it incredibly difficult to write software that accurately determines the intended meaning of text or voice data. Several NLP tasks break down human text and voice data in ways that help the computer make sense of what it's ingesting.
Natural Language Processing (NLP) is branch of computer sciences that deals with teaching computers to understand human language, as human actually use it. Then NLP analyses their sentiments, categorize them into buckets, or recognize specific named entities.
Natural language generation is sometimes described as the opposite of speech recognition or speech-totext; it is the task of putting structured information into human language
We will employ NLTK (Natural Language Toolkit) for crucial tasks such as tokenization, parsing, classification, stemming, tagging, and semantic reasoning in our fraud detection system. NLTK not only offers these essential NLP capabilities but also provides graphical demonstrations and access to sample datasets tailored for fraud detection.
They leverage the probability of word occurrences within a sequence of words to estimate the relative likelihood of different fraudulent activities and patterns.
Through the power of NLP, our system will introduce an auto-reply feature, enabling swift responses to potential fraudulent activities
Deep studying is a type of device studying primarily based on artificial neural networks that makes use of a couple of layers of processing to extract steadily better-degree functions from statistics.
Fraud Detection with Deep Neural Networks (DNNs): characterized by their multiple hidden layers, play a pivotal role in deciphering the intricate patterns within fraudulent financial activities
Uncovering Deception with Convolutional Neural Networks (CNNs): By revealing hidden connections among seemingly unrelated transactions, CNNs substantially enhance the effectiveness of fraud detection algorithms.
Tracking Temporal Fraud Patterns with Recurrent Neural Networks (RNNs): RNNs excel at capturing temporal dependencies and sequential patterns, making them well suited for the identification of evolving or recurring fraudulent behaviors within financial systems.
Our techniques
Data Storage and Comparison: Deep Learning Storage: Our model employs deep learning techniques to store and manage data from both students and universities efficiently.
Data Comparison: Deep neural networks are utilized to compare the data submitted by students with the data provided by universities. This ensures that the information aligns and helps identify any discrepancies.
Identity Verification and Income Assessment: Identity Verification: Our model utilizes deep learning for identity verification. It scans and analyzes personal information such as identity numbers, names, and surnames provided by students. It crossreferences this data with information from the Home Affairs database to determine if the student exists. Income Assessment: The system uses deep learning techniques to assess household income. It checks whether the total monthly household income, obtained from the Home Affairs data, exceeds the specified threshold (e.g., R300, 000). If the income exceeds the threshold, the application is blocked.
Application List Analysis: Comparing University Application Lists: Deep learning is employed to analyze application lists received from universities. The model first verifies the existence of each student by cross-referencing his or her identity numbers with Home Affairs data. If a student's identity number does not exist, the system automatically blocks the application and alerts the institution.
Confirmation Security Techniques: SMS Confirmation: Deep learning is used to send SMS messages to students to confirm their application. This SMS confirmation process helps ensure that the applicant is indeed the student they claim to be. Email Confirmation: Email confirmation is another layer of security. The system employs deep learning to manage and send confirmation emails to applicants for additional validation. Face Recognition: For added security, our model utilizes deep learning-based face recognition techniques. It matches the faces of applicants with images stored in the institution's database. Only when a match is confirmed through face recognition is an application approved