Prediction - 🔍 Anticipating AI's Big Shift in 2025: OpenAI’s Focus on Domain-Centric Solutions

Prediction:

OpenAI is set to shift towards domain-centric solutions, making 2025 a transformative year for AI. This transition is based on the data collected and learned from APIs serving different domains, focusing on context window improvements, reasoning patterns, and cross-modal integration. This will significantly enhance decision-making in critical sectors like FinTech and healthcare. By tackling technical challenges and integrating user feedback, these advancements will result in more powerful, tailored AI applications that will reshape entire industries.

Expanding Beyond Language Models

Today, OpenAI is primarily recognized as a leading provider of large language models, but its true capabilities extend much further. Its question-answering abilities, for instance, are exceptionally powerful and evolving rapidly. As clients integrate this technology into critical sectors like FinTech and healthcare, they will unlock new levels of context window improvements, and cross-modal integration and reasoning by adopting techniques like tree of thought, chain of thought, and graph-based approaches, enabling AI to think and deduce more effectively. Feedback from users will be pivotal in this journey, guiding organizations on how best to structure information flows and assess when to fine-tune models, use Retrieval-Augmented Generation (RAG), or determine the optimal use of short-term and long-term memory. This constant feedback loop will allow AI to achieve unprecedented levels of contextual understanding and adaptive reasoning, creating models that align more closely with complex real-world needs. 

"OpenAI's journey is no longer just about language—it's about thought and contextual adaptation."

Building Resilient and Adaptive Systems

These advancements will likely lead to the development of more resilient and adaptable systems. Future systems will not only enhance decision-making but also push reasoning capabilities into new territories, setting the stage for increasingly sophisticated agents and refined RAG architectures. These improved architectures are expected to reduce hallucinations, boost accuracy, and lead to products that are more responsive to real-world challenges. Overcoming issues like catastrophic forgetting, hallucinations, and knowledge manipulation will be critical, positioning these systems as robust, reliable solutions across industries. 

"Resilient, adaptive AI systems will transform decision-making and redefine industry standards."

Addressing Technical Challenges

Currently, accuracy challenges remain in areas such as domain-relevant embedding, balancing retrieval techniques against accuracy and latency, chunking methods based on usage or query types, contextualization, and routing or re-ranking processes. Yet, these elements are essential for advancing the capabilities of AI models. Despite these ambiguities, ongoing data processing and analysis are paving the way for more focused, domain-specific AI products. Within the next six to eight months, we’re likely to see a new wave of AI-driven applications, from highly specialized agents to RAG applications and APIs crafted for specific industries.

 "Technical hurdles are simply steps toward the next wave of AI-driven, domain-specific innovation."

The Transformative Potential of 2025

The year 2025 is set to be a pivotal moment in AI, marking the dawn of domain-centric solutions that will reshape how AI interacts with our world. As more industry-specific applications emerge, OpenAI’s technologies will bring powerful, tailored solutions closer to reality. 

"2025: The year AI becomes truly domain-centric, reshaping industries with precision, customized models, and highly accurate agents and RAG systems."

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#AI #OpenAI #DomainSpecificAI #Innovation #MachineLearning #FinTech #Healthcare #FutureOfAI

Fintech - ML Lens - Stocks

Peter Lynch is one of the best investors in the world. In his playbook. Most of his areas of investing if you relook in ML terms - Find features which will help to classify models in these categories and invest accordingly.

• Investment Distribution (This is also an ML optimization area, Operations research area)
• Risk vs Returns classification (Apply ML lens)

Ref - Link

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Fintech - Product Inspirations

This could be part of Navi / Digital Apps

Product Ref - Link

Dashboard to List Customers / Risks (ML Classification)

Risk behavior for repayment (ML / Classification / Forecast)

Digital / Installments / Reminders / Choose your collection Strategy

Keep Exploring!!!

Document Q&A

From OCR, Document Extraction, Understanding, Hugging face has come a long way :)

DocQnA Pipeline very impressive

Results

Keep Exploring!!!

• TesserOCR
• MMOCR
• OCRmypdf
• EasyOCR
• PaddleOCR
• Kraken
• OCRopus
• PyOCR
• Tesseract

OCR, Fintech and Usecase

Keep Learning!!!

Fraud Detection Research Papers

Fraud Detection Research Papers

Paper #1 - Credit Card Fraud Detection in e-Commerce: An Outlier Detection Approach

Notes

• No prior knowledge of outliers or inliers is needed
• The proposed algorithm is easy to scale as it can easily be implemented in a distributed manner
• Proposed algorithm is general in nature and does not require k-means algorithm as the only base clustering algorithm. 
• Can estimate a measure of consistent behavior (good behavior) for each data point then we can identify outliers as data points with low consistency score.
• Attempt the problem of outlier detection by estimating a consistency score
• In our experiments we found that incrementally increasing k with a xed step works just as well as the ensemble created by carefully selecting k using a principled approach such as Silhouette Score
• For #Fraud #detection with limited dataset, Algorithms to get started to find potential transactions #IsolationForecast, #OneClassSVM, #Clusteringbasedoutlierdetection

Paper #2 - A Comparison Study of Credit Card Fraud Detection: Supervised versus Unsupervised

Notes

• 6 supervised classification models, i.e., Logistic Regression (LR), K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Decision Tree (DT), Random Forest (RF), Extreme Gradient Boosting (XGB)
• 4 unsupervised anomaly detection models, i.e., One-Class SVM (OCSVM), Auto-Encoder (AE), Restricted Boltzmann Machine (RBM), and Generative Adversarial Networks (GAN)
• Supervised Learning Methods
• Logistic regression allows us to estimate the probability of a categorical response based on one or more predictor variables x.
• KNN algorithm essentially boils down to forming a majority vote between the K most similar instances to a given unseen observation
• SVM is to derive an optimal hyperplane that maximizes the margin between two classes
• Decision trees are simple but intuitive models that utilize a top-down approach in which the root node creates binary splits until a certain criteria is met
• XGB uses gradient descent for optimization to improve the predictive accuracy at each optimization step by following the negative of the gradient as we are trying to find the sink in a n-dimensional plane

Unsupervised Learning Methods

• OneclassSVM - The algorithm learns a soft boundary in order to embrace the normal data instances using the training set, and then, using the testing instance, it tunes itself to identify the abnormalities that fall outside the learned region
• RBM model consists of visible and hidden layers, which are connected through symmetric weights. The objective of the generative training in RBM is to learn the unknown (h) iteratively using the input (x).
• An auto-encoder (AE) learns to map from input to output through a pair of encoding and decoding phases
• GAN AnoGAN by simultaneously learn an encoder E that maps input samples x to a latent representation z, along with a generator G and discriminator D during training.

Paper #3 - xFraud: Explainable Fraud Transaction Detection

Key Notes

• Fraudster user detection
• Fraud transaction detection
• Methods that do not need to define meta-paths a priori, instead are able to automatically learn these patterns using a GNN.

• xFraud detector. We are inspired by Transformer [39] and HGT [18], when designing the xFraud detector incl. heterogeneous mutual attention and heterogeneous message passing with key, value, and query vector operations (self-attention mechanism).

Paper #4 - TitAnt: Online Real-time Transaction Fraud Detection in Ant Financial

Key Notes

• Rule-based methods have been extensively studied over the years [46] for fraud detection problem
• several unsupervised learning and anomaly detection methods are introduced
• Recurrent neural network to exploit temporal information of account behavior
• Anomaly detection methods, such as isolation forest sheds light on fraud detection tasks

Paper #5 - A Comprehensive Survey on Machine Learning Techniques and User Authentication Approaches for Credit Card Fraud Detection

Key Notes

• Combination of Hidden Markov Model (HMM) and K-Means algorithms was used in (Kumari and Choubey, 2017) to identify the fraudulent activities on credit cards
• A transaction is considered suspicious if its distance to the center of the cluster exceeds a pre-set threshold
• Self-Organizing Map (SOM) is an unsupervised neural network learning model, which has been used to form customer profiles and visualize fraudulent patterns

Paper #6 - A Survey of Credit Card Fraud Detection Techniques: Data and Technique Oriented Perspective

Key Notes

• A Hidden Markov Model is a double embedded stochastic process which is applied to model much more complicated stochastic processes as compared to a traditional Markov model
• Genetic algorithms have been used in data mining tasks mainly for feature selection. 
• A Bayesian network is a graphical model that represents conditional dependencies among random variables. The underlying graphical model is in the form of directed acyclic graph

More Reads

• Credit card fraud detection using machine learning: A survey
• Machine Learning for Fraud Detection in E-Commerce: A Research Agenda
• CREDIT CARD FRAUD DETECTION USING MACHINE LEARNING: A STUDY
• Deep Learning Methods for Credit Card Fraud Detection
• Locally Interpretable One-Class Anomaly Detection for Credit Card Fraud Detection
• Security in Next Generation Mobile Payment Systems: A Comprehensive Survey
• Profiling US Restaurants from Billions of Payment Card Transactions
• An AI-powered Smart Routing Solution for Payment Systems
• Evaluating Serverless Architecture for Big Data Enterprise Applications
• Towards Demystifying Serverless Machine Learning Training
• You Do Not Need a Bigger Boat Recommendations at Reasonable Scale in a (Mostly) Serverless and Open Stack
• PREDICTING NEXT SHOPPING STAGE USING GOOGLE ANALYTICS DATA FOR E-COMMERCE APPLICATIONS
• Analyzing and Predicting Purchase Intent in E-commerce: Anonymous vs. Identified Customers

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Indian #Fintechstartups #MLOpportunities #Navi #Credavenue #Lendingkart

Indian #Fintechstartups #MLOpportunities #Navi #Credavenue #Lendingkart - ML Opportunities, Use cases, Domain Specific Features 

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Fintech Research papers

Paper #1 - Credit risk prediction in an imbalanced social lending environment

Key Notes

• Credit risk prediction is an effective way of evaluating whether a potential borrower will repay a loan
• Borrowers benefit from lower interest rates; lenders receive a higher return than they would from a bank
• Class imbalance is a common problem in loan default prediction
• The under-sampling approach includes random under-sampling (RUS), and instance hardness threshold(IHT) algorithms
• For over-sampling approach, random over-sampling (ROS), synthetic minority over-sampling technique (SMOTE), and adaptive synthetic sampling (ADASYN) are studied
• publicly available datasets released by the Lending Club, a well-known P2P lending platform(lendingclub.com)

Paper #2 - Machine Learning in FinanceEmerging Trends and Challenges 

Key Notes

• inevitable trust deficit in deploying them in critical and privacy-sensitive applications, the so-called “black-box” nature of such models
• Risk modeling - operational risk management, compliance, and fraud management  
• Portfolio management: The portfolios are designed based on the recommendations of smart algorithms that optimize various parameters with return and risk being the two most important ones
• Algorithmic trading: Algorithmic trading exploits the use of algorithms to carry out stock trading in an autonomous manner with the minimal human intervention
• Fraud detection and analysis: Fraud detection and analysis is one of the most critical machine learning applications in the finance industry
• Financial chatbots

Paper #3 - Recommendation Engine for Lower Interest Borrowing on Peer to Peer Lending (P2PL) Platform

Key Notes

• a recommendation framework for borrowers to help them borrow with lower interest rates
• Bidding loan: first and foremost, borrowers themselves decide the maximum interest rate they are willing to pay.
• machine learning models to classify if a given borrower will succeed on the bidding loan platform
• machine learning models to predict the interest rate payable for bidding and traditional loans

• contains 12,006 loans (both funded and nonfunded loans) with 12 features and 2 response variables — the borrower’s interest rate and the status of the bidding loan

• Predicting the success rate of funding bidding loans

Paper #4 - Determinants of Interest Rates in the P2P Consumer Lending Market: How Rational are Investors?

Key Notes

• The (1) loan-specific view analyzes elements such as loan volume and the loan period by investigating the effects of these elements on the interest rate for P2P consumer loans
• (2) borrower-specific factors focus on aspects that affect a borrower's credit rating

Paper #4 - Deep Learning for Financial Applications : A Survey

Key Notes

Paper #5 - MACHINE LEARNING ALGORITHMS FOR FINANCIAL ASSET PRICE FORECASTING

Investment professionals often refer to this non traditional data as “alternative data" [12]. Examples of alternative data include the following:

• Satellite imagery to monitor economic activity. Example applications: Analysis of spatial car park traffic to aid the forecasting of sales and future cash flows of commercial retailers. Classifying the movement of shipment containers and oil spills for commodity price forecasting [13]. Forecasting real estate price directly from satellite imagery [14].
• Social-media data streams to forecast equity prices [15], [16] and potential company acquisitions [17].
• E-commerce and credit card transaction data [18] to forecast retail stock prices [19].
• ML algorithms for patent analysis to support the prediction of Merger and Acquisitions (M&A)

Capital Asset Pricing Model (CAPM)

The CAPM holds the following main assumptions:

• One-period investment model: All investors invest over the same one-period time horizon.
• Risk averse investors: This assumption was initially developed by Markovitz and asserts that all investors are
• rational and risk averse actors in the sense that when choosing between financial portfolios investors aim to optimize the following:
• (a) Minimize the variance of the portfolio returns.
• (b) Maximize the expected returns given the variance.
• Zero transaction costs: There are no taxes or transactional costs.
• Homogenous information: All investors have homogenous views and information regarding the probability distributions of all security returns.

• In the context of financial asset price forecasting the information processing problem we are trying to solve is the prediction of an asset price t time steps in the future - we are effectively trying to solve a non-linear multivariate
• time series problem

Paper #6

FinBrain: When Finance Meets AI 2.0

More Reads

• From banks to DeFi: the evolution of the lending market
• Buy Now, Pay Later (BNPL) ...On Your Credit Card∗
• Data science and AI in FinTech: An overview

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Creditworthiness and Features used in Machine Learning models

Some readings on features used.

Paper #1 - Determining Secondary Attributes for Credit Evaluation in P2P Lending

Key Features

Paper #2 - Credit Scoring for Good: Enhancing Financial Inclusion with Smartphone-Based Microlending

Key Notes

1. Socio-Demographic: including bank history, income and debit account behavior.
2. Calling Behavior: aggregated values for number and duration of phone calls made and received on different days and at different times of the day.
3. Link-Based: counts of the number of good and bad credit card holders in each customer’s egonet.
4. Influence Score: the scores each customer obtained after two distinct influence propagation algorithms were applied to the network.

Paper #3 - Machine Learning approach for Credit Scoring

Key Notes

• LIME, Local Interpretable Model-agnostic Explanations, is a novel technique that explains the predictions of any classifier in an interpretable and faithful manner
• SHAP, which stands for (SHapley Additive exPlanation) [23], is a novel approach for model explainability which exploits the idea of Shapley regression value16 to model feature influence scoring.

Paper #4 - A comparative study of forecasting Corporate Credit Ratings using Neural Networks, Support Vector Machines, and Decision Trees

Key Notes

More Reads

Learning Latent Representations of Bank Customers With The Variational Autoencoder

Eliciting Social Knowledge for Creditworthiness Assessment

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Weekend Lessons - Humans & AI in Financial Services - The Future - Samik Chandarana | ODSC Europe 2019

Key Notes

• Being a thought leaders matters
• Connect and finite transformative ideas

• Why we do, when we do
• Intelligently automating
• Age-old process

• Business Aligned outcomes
• Long term thinking - research projects
• Dream lot / Dream big

• AI for trading
• Suggest probable matches
• Portfolio management
• Reconciliation using AI

• Data Remediation - People to cleanup data
• Libor Transition - Financial crimes
• Digitize information
• Market Surveillance Toolkit
• Get data in order
• Organize information in good dashboard
• Email - Intent extract - Get info - Manual Validation - Notify
• AI is young science
• NLP, Vision, Search

• Finding money graph/ traffic
• Routes used

Keep Thinking!!!

cs229 Interesting Projects - FinTech

Project #1 - Loanliness: Predicting Loan Repayment Ability by Using Machine Learning Methods

Key Notes

• Evaluating and predicting the repayment ability of the loaners is important for the banks to minimize the risk of loan payment default

Consumer Financial Protection Bureau rules

• Expected income or assets
• Employment status
• Expected monthly payment
• Monthly payment on the simultaneous loans
• Monthly payment of the mortgage
• Current debt status
• Residual income
• Credit history

Data Pre-processing

• Feature concatenation
• Feature Encoding and Normalization
• Invalid/Empty Entry Replacement
• Polynomial feature transformation

Summary - Data pre-processing, classification algorithms lessons

Code - Link

Project #2 - Detecting Credit Card Fraud with Machine Learning

Key Notes

Class imbalance solutions

• Undersampling – balances the data by randomly choosing observations from the majority class to exclude
• Oversampling – balances the data by randomly oversampling the minority class
• Both – a hybrid method that employs both undersampling and oversampling
• ROSE – a synthetic data generation method that balances the data by creating artificial samples of the minority class in the neighborhood of existing examples

Models

• Logistic Regression with Quadratic Terms and LASSO Regularization
• Simple Logistic Regression
• Random Forests
• Neural Networks

Summary - Handling imbalanced data, Applying different ML algos

Project #3 - Algorithmic Trading using LSTM-Models for Intraday Stock Predictions

Key Notes

• Feature Extraction - min-max-scalar

Keep Thinking!!!