Enterprise Fintech AI Workflow and Automated Risk Intelligence Platform

@corpmember29

Hi, I can build this fintech AI risk platform end-to-end, replacing manual onboarding, document verification and risk assessment with secure automated workflows. I have 13+ years of experience in AI, Python, APIs, automation and enterprise systems. I’ll implement: * Secure onboarding and document ingestion * OpenAI + Claude financial document analysis * Python-based risk and fraud scoring * Credit/fraud API integrations * n8n/Make workflow automation * Automated approval and human-review routing * CRM + Slack alerts * Power BI/Tableau executive dashboards * Audit logs, RBAC and secure data handling I’ll build the architecture modularly so APIs, AI models and risk rules can be updated easily. I’m ready to review your existing systems and define the architecture, milestones and implementation plan.

@ahmadayaz

As an AI Systems Architect with a proven track record, I specialize in designing and deploying Enterprise Fintech AI Workflow and Automated Risk Intelligence Platforms. I excel in implementing intelligent data ingestion, risk analysis using AI, integrating language models for financial document parsing, and automating workflows across platforms. Additionally, I have experience in creating executive dashboards and reporting systems for real-time insights and compliance monitoring. For optimal results, I would require further details on data scale, compliance standards, system integrations, and dashboard customization preferences. I aim for a long-term partnership to not only achieve immediate project goals but also continuously optimize the platform for scalability and regulatory changes. Collaborating on your ambitious goals, we can create a cutting-edge solution that surpasses expectations and supports your growth in the fintech industry. I am eager to discuss how we can realize your vision and propel your organization forward.

@sajjadtaghvaeifr

Hello, I trust you're doing well. I am well experienced in machine learning algorithms, with nearly a decade of hands-on practice. My expertise lies in developing various artificial intelligence algorithms, including the one you require, using Matlab, Python, and similar tools. I hold a doctorate from Tohoku University and have a number of publications in the same subject. My portfolio, which showcases my past work, is available for your review. Your project piqued my interest, and I would be delighted to be part of it. Let's connect to discuss in detail. Warm regards. please check my portfolio link: https://www.freelancer.com/u/sajjadtaghvaeifr

@hoangnn90

I have extensive experience designing enterprise-grade AI workflows and automation platforms for fintech and financial services. I can architect a secure, scalable pipeline integrating advanced LLMs, API integrations, and ETL processes, ensuring real-time risk assessment and seamless data flow. My approach emphasizes robust automation, compliance, and insightful reporting through Power BI and Tableau, delivering a fully autonomous, production-ready system within your timeline. I’m confident I can help transform your manual processes into an efficient, AI-driven risk intelligence platform.

@DeveloperStation

Hi, We built an internal AI workflow engine that encodes domain knowledge into automated, evidence-backed pipelines using OpenAI and Claude, exactly the kind of LLM-driven decision routing this needs. BD Automation: internal AI-driven business workflow with OpenAI and Claude APIs. One design choice matters early: for parsing balance sheets and tax returns, LLM output alone is risky for compliance. I'd pair the LLM extraction with deterministic Python validation against your risk matrix, so numbers get checked, not just generated. On a past contract I built a secure ingestion pipeline with that split for a client who needed repeatable, auditable processing. I've also shipped ChatGPT API integrations with Stripe, login, and admin panels end to end. Question: which third-party credit and fraud APIs are you planning to use, and do you already have keys? That decides the ingestion layer. I'd start with a first milestone on the ingestion and LLM parsing so you only release on working output. Adil

@C0RETECHIES

Hi there, I see you're building an autonomous risk assessment engine. It ingests onboarding documents, uses LLMs to parse unstructured financials, queries third-party APIs for credit/fraud data, and applies a risk matrix. Low-risk profiles are auto-processed into your CRM and a Power BI dashboard, while anomalies trigger Slack alerts for compliance review. Technical approach: We'll use n8n to orchestrate the entire workflow. Python scripts will manage ETL and API calls to LLMs (OpenAI/Claude) for parsing documents. A central PostgreSQL database will stage the structured data for the risk engine and feed the Power BI dashboard. Core modules: Data Ingestion (API/Webhooks), LLM Document Analysis, Risk Scoring Engine with conditional routing, and a BI/Alerting layer (Power BI, Slack, CRM). Relevant systems: We built a multi-agent AI pipeline in n8n that performs similar data enrichment, scoring, and CRM updates. We also have an AI assistant that integrates n8n with Slack and multiple LLMs for complex reasoning tasks. We recommend an MVP-first approach: prove out the LLM parsing for a single document type and the core risk logic. Then, we'll build out the full n8n workflow, integrations, and finalize the dashboard. This validates the core intelligence early. Regards, Rohit

@maxscender

I can help you replace manual risk and onboarding bottlenecks with a decision-grade automation pipeline that’s auditable, scalable, and built for fintech compliance. I’d start by mapping your current data flow and identifying where documents, verification checks, and risk decisions get stuck. Then I’d build secure ingestion via webhooks and connectors, clean and structure the data in Python, use LLMs to extract and validate information from financial statements, and orchestrate everything through n8n/Make with conditional routing—auto-approving low-risk profiles, flagging anomalies, updating CRM, and notifying Slack in real time. The Power BI dashboard would give executives live visibility into portfolio risk, processing throughput, and compliance bottlenecks—with every decision traceable and exceptions surfaced immediately, not silently buried.

@PINESCRIPTERR

Hi, I can architect this as an auditable fintech decision-support platform combining deterministic risk rules, verified third-party data, document intelligence, workflow orchestration, and executive reporting. The ingestion layer would use authenticated APIs/webhooks, malware-scanned document storage, schema validation, lineage, and idempotent processing. Public compliance data would be collected only from lawful, permitted sources; restricted scraping or access-control bypasses would not be used. LLMs should extract and classify unstructured documents into validated schemas with page-level evidence, confidence scores, and reconciliation against numerical calculations performed in Python. They should not independently determine creditworthiness or compliance outcomes. Final scoring would use versioned risk policies, explainable features, reason codes, model/rule audit trails, and human approval for adverse, ambiguous, or high-impact decisions. n8n/Make can orchestrate notifications and low-risk operational steps, while sensitive decision logic remains in a tested backend service. Integrations will use least-privilege credentials, encrypted secrets, retry/dead-letter handling, and full event logs. Power BI/Tableau datasets will expose governed metrics without leaking unnecessary personal data. Regards, Houssame

@installerpoint

Hello, I’m interested in building the automated risk intelligence and onboarding platform described in your requirements. I understand this is more than a collection of automations—you need a reliable, auditable workflow that can ingest sensitive financial information, extract structured data from documents, apply risk rules and AI-assisted analysis, integrate external verification services, and route decisions to the appropriate teams. Automated executive summaries can also be generated and distributed on a scheduled basis. Production considerations For a fintech environment, I would prioritize security, auditability, traceability, and failure handling from the beginning. Every significant processing step should have an audit trail covering input, output, model/rule version, timestamp, decision signals, and human intervention where applicable. I would also design the system so that external API failures or LLM availability issues do not silently produce incorrect decisions. Failed or uncertain cases should be routed into a controlled review queue rather than automatically approved. I can work with your existing infrastructure and preferred cloud/database environment and provide clean, maintainable Python services and automation workflows. I’d be happy to start by reviewing your existing systems, data sources, risk matrix, and required third-party APIs, then turn the requirements into a concrete production architecture and implementation plan. Best regards ADEEL

@rajat07me

This is a serious automation build, and I understand the moving parts involved here — ingestion pipelines, LLM-based document parsing, risk routing logic, CRM/Slack automation, and executive reporting. I have strong experience with AI workflow design, API integrations, and multi-platform automations, so I can help you turn this into a structured, production-ready system rather than a patchwork setup. For a fintech flow like this, security, reliability, and clean decision logic matter as much as the automation itself. If you'd like, I can map the implementation in phases and help define the smartest build path from day one.

@BarryCrouch

Automating fintech risk assessment relies on structured prompt engineering with strict JSON schema outputs when evaluating unstructured balance sheets and audit files via Claude or OpenAI APIs. Combining Python data handling scripts with n8n workflow triggers creates a reliable middleware layer that evaluates credit API parameters against risk scoring thresholds. Pushing processing metrics directly to Power BI via REST APIs guarantees executive dashboards reflect onboarding throughput and flagged compliance anomalies in real time. I can share a quick workflow execution draft to illustrate the conditional risk routing logic. Do you have specific third-party financial verification APIs already selected for credit and fraud checks, or should we incorporate API connector templates?

@huzaifa306

Hi, I can build your AI risk and onboarding workflow using n8n, Make, Python, and LLMs, with automated document analysis, risk scoring, API integrations, and compliance alerts. I have 5+ years of experience in AI automation, workflow design, APIs, and data processing, and can build a secure and scalable system with clear reporting and human review controls. Best regards, Huzaifa

@muneeb6767

I can architect and build this fintech risk platform using Python, LLM APIs, n8n/Make, secure webhooks, ETL pipelines, CRM integrations, and Power BI/Tableau dashboards. I’ll focus on auditable risk scoring, document extraction, conditional human-review workflows, real-time alerts, and production-ready automation across the full onboarding lifecycle.

@sarimshoaib1425

100% doable. Big scope, but the pipeline logic is clear. Built an automated risk and reporting pipeline for a UAE client, processing 18000 transactions through Power Automate and Power BI. My approach is a little different though. I'd run n8n as the orchestration core, LLMs parsing unstructured financial docs into structured risk data. Power BI reads live off the same pipeline, Slack and CRM alerts fire on anomalies automatically. Like I said, 100% doable. First working pipeline in about three weeks. Feel free to DM me for case studies. Or check the projects on my profile. Let's do it.

@krupnovdenis

Hello! We can build an automated risk and onboarding workflow for this fintech platform. 1. What should be the first workflow to automate? 2. Which systems and APIs need to be connected first? — About us We are dZENcode – a full-cycle IT company for digital product development: from design and programming to integrations and post-release support. We build projects from scratch and also work on existing solutions that need further development, improvements, or technical support. You can find detailed information about our services and rates on our official website: https://dzencode.com. Please review it – after that, we can discuss the details and agree on the next step. ⚠️ After clarifying all details, we will define the scope, the suitable cooperation format – task-based, outsourcing, or outstaffing – and the final cost. Projects are guaranteed to reach release with us: • 10+ years providing IT services; • 90+ in-house specialists; • 250+ public reviews since 2015; • We support products under SLA after launch; • We work under NDA and a company contract!

@Aaleen110

The real work here is taking unstructured financial statements and feeding them into an executive dashboard, which means LLMs are the core of processing the data, and then getting that into Power BI for real-time alerts and risk scoring. I will build this using Python for the core logic and LLM integration, connecting to your third-party APIs for credit and fraud checks with n8n for workflow automation, orchestrating the entire pipeline from file ingestion to final dashboard update. I would not use a general-purpose ETL tool for this; it would be slower and less flexible than building a custom Python solution integrated with an automation platform like n8n which can handle the conditional routing and API calls precisely. The system will ingest user onboarding files, execute real-time credit and fraud verification checks via third-party financial APIs, process unstructured financial statements utilizing LLMs, and route qualified profiles into an executive Power BI KPI dashboard while instantly alerting compliance. Track record on here: 100% on time, 100% on budget, 5.0 across 8 reviews. What are the specific third-party financial APIs that will be used for credit and fraud verification checks? I will send back a detailed architecture diagram and a phased implementation plan once I have the list of APIs.

@dataspro

Nice to meet you , It is a pleasure to communicate with you. My name is Anthony Muñoz, I am the lead engineer for DSPro IT agency and I would like to offer you my professional services. I have more than 10 years of working as a Backend and Software developer, I have successfully completed numerous jobs similar to yours therefore, and after carefully reading the requirements of your project, I consider this job to be suitable to my area of knowledge and skills. I would love to work together to make this project a reality. I greatly appreciate the time provided and I remain pending for any questions or comments. Feel free to contact me. Greetings

@mughiraaa

Hello, I have already completed similar fintech and AI automation projects involving document processing, risk workflows, API integrations, LLMs, and business dashboards. I have strong experience in web development, APIs, and scalable software solutions, so I can quickly understand your compliance workflow and deliver exactly what is needed. I can connect financial APIs, process financial documents with OpenAI or Claude, automate decisions through n8n and Make, and send the right data to Power BI, CRM, and Slack. How are your current risk matrices and approval rules defined, and can you provide the rules that should trigger automatic approval or human review? I would be happy to schedule a quick meeting to review your current systems, data flow, and security requirements and discuss the best approach. I will share my portfolio in chat I look forward to hear from you. Thanks Best Regards, Mughira

@jonasdedzo

Hello. I’d love to help design and build this AI-driven fintech risk intelligence platform. This is exactly the type of system where a strong combination of AI, automation, data engineering, and secure workflow architecture can create significant business value by reducing manual compliance effort and improving decision speed. I can help build the complete pipeline — from secure data ingestion and document processing to AI-powered risk analysis, automated workflows, and executive reporting. The solution can combine Python-based processing, OpenAI/Claude integrations, API connectors, n8n/Make automation, CRM integrations, and BI dashboards to create a reliable end-to-end risk assessment engine. My approach would focus on building a scalable architecture with clear separation between data ingestion, AI analysis, decision logic, automation workflows, and reporting. Financial documents such as statements, audits, and tax files can be processed using LLM-based extraction combined with structured validation rules and risk scoring models, while automation handles routing, alerts, and approvals. I have experience working with AI APIs, automation platforms, data pipelines, backend systems, and business intelligence solutions. I focus on production-ready systems with security, maintainability, and future expansion in mind. I’d be happy to review your existing workflows, API requirements, and compliance process to define the best architecture, milestones, and delivery plan.