Bank Risk Controller System with Automated Dashboard

Introduction

This project is focused on building an automated system to monitor and control bank risk factors, integrated with a dynamic dashboard for real-time insights. The goal is to streamline risk assessment processes using Python-based data analysis and visualization tools.

Skills Takeaway:

-> Python scripting -> Data extraction and preprocessing -> Risk scoring algorithm development -> Dynamic dashboard using Streamlit -> Real-time risk insights

Overview

Data Collection and Processing:

Automate the extraction of financial and operational data, focusing on risk indicators, including: -> Credit risk metrics -> Market risk exposure -> Operational risks -> Liquidity ratios -> Customer behavior analytics

Dynamic Filtering:

Develop a dashboard with intuitive filters, allowing users to explore risk metrics by department, branch, risk type, or time period.

Risk Scoring Algorithm:

Build and implement algorithms for assessing creditworthiness, liquidity risk, and market risk. Generate comprehensive risk scores.

Data Storage: Store the processed data in structured formats (e.g., CSV, JSON, or databases). For large-scale operations, integrate with MySQL to enable fast data querying.

Data Analysis and Visualization: Streamlit Dashboard: -> Create an interactive dashboard to visualize risk metrics. -> Provide insights through charts, tables, and heatmaps.

Real-time Updates: -> Enable users to input parameters dynamically and get instant updates on risk exposure. -> Display risk trends, aggregations, and potential red flags.

Data Analysis: -> Analyze risk exposure per branch or department. -> Identify trends such as the most common credit risks or periods of high market volatility.

Technology and Tools: -> Python -> Streamlit (for interactive dashboards) -> MySQL (optional, for large datasets) -> Pandas (for data manipulation) -> Scikit-learn (optional, for machine learning-based risk scoring models) -> Matplotlib/Plotly (for visualizations)

Packages and Libraries: -> Pandas: For data manipulation and preprocessing.

import pandas as pd -> Streamlit: For creating the dashboard.

import streamlit as st -> MySQL Connector: For database integration.

import mysql.connector -> Matplotlib/Plotly: For visualizing complex risk data.

import matplotlib.pyplot as plt import plotly.express as px -> Scikit-learn: For building predictive models for risk assessment (optional).

from sklearn.ensemble import RandomForestClassifier Features: Risk Monitoring: -> Collect, preprocess, and analyze key risk indicators. -> Build algorithms to calculate overall risk scores.

Dynamic Filtering: -> Filter risk metrics by:

Department/Branch Risk type (credit, operational, market) Timeframe Interactive Visualization: -> Display metrics as tables, graphs, and heatmaps. -> Highlight high-risk areas dynamically based on input.

Usage: Risk Assessment: -> Input key parameters or datasets. -> Automate calculations of risk scores and generate a comprehensive report.

Dynamic Dashboard: -> Use the sidebar to apply filters for branch, risk type, or timeframe. -> Visualize results through charts and heatmaps.

Insights Generation: -> Analyze high-risk areas to prioritize mitigation strategies. -> Monitor trends to preemptively address emerging risks.

Contact: LinkedIn: https://www.linkedin.com/in/suruthi-boopalan/ Email: suruthipriya50@gmail.com