Autonomous Agents for Financial Management in U.S. Businesses

import pandas as pd
from sklearn.linear_model import LinearRegression

# Load historical cash flow data
df = pd.read_csv('historical_cash_flow.csv')

# Prepare data for forecasting
X = df[['month', 'year']]
y = df['cash_flow']

# Initialize and train the model
model = LinearRegression()
model.fit(X, y)

# Predict future cash flows
future = pd.DataFrame({'month': [1,2,3,4,5,6,7,8,9,10,11,12],
                       'year': [2025]*12})
predictions = model.predict(future)

df_predictions = pd.DataFrame({'month': future['month'], 'predicted_cash_flow': predictions})
df_predictions.to_csv('predicted_cash_flow.csv', index=False)
        

{
  "predicted_cash_flow": {
    "January": 150000,
    "February": 160000,
    "March": 155000,
    "April": 170000,
    "May": 165000,
    "June": 175000,
    "July": 180000,
    "August": 178000,
    "September": 185000,
    "October": 190000,
    "November": 195000,
    "December": 200000
  }
}
        

1. Collect historical financial data.
2. Clean and preprocess the data.
3. Develop and train predictive models.
4. Generate and validate cash flow forecasts.
5. Export predictions in JSON or CSV format.

import pandas as pd
from statsmodels.tsa.seasonal import seasonal_decompose

# Load sales data
df_sales = pd.read_csv('sales_data.csv', parse_dates=['date'], index_col='date')

# Decompose the sales data to identify seasonality
result = seasonal_decompose(df_sales['sales'], model='additive', period=12)
df_sales['seasonal'] = result.seasonal

# Adjust cash flow predictions based on seasonal trends
df_predictions = pd.read_csv('predicted_cash_flow.csv')
df_predictions['adjusted_cash_flow'] = df_predictions['predicted_cash_flow'] * (1 + df_sales['seasonal'].mean())
df_predictions.to_csv('adjusted_cash_flow.csv', index=False)
        

{
  "adjusted_cash_flow": {
    "January": 152000,
    "February": 162000,
    "March": 157000,
    "April": 172000,
    "May": 167000,
    "June": 177000,
    "July": 182000,
    "August": 180000,
    "September": 187000,
    "October": 192000,
    "November": 197000,
    "December": 202000
  }
}
        

1. Fetch seasonal trend data from external sources.
2. Analyze and map seasonal patterns to historical cash flow data.
3. Adjust predictions using identified seasonal factors.
4. Validate adjusted forecasts against market indicators.
5. Export the adjusted forecasts in the required format.

import pandas as pd

# Load data from different departments
sales_data = pd.read_csv('sales_data.csv')
procurement_data = pd.read_csv('procurement_data.csv')
operations_data = pd.read_csv('operations_data.csv')

# Merge datasets on common keys
consolidated_data = pd.merge(sales_data, procurement_data, on='date')
consolidated_data = pd.merge(consolidated_data, operations_data, on='date')

# Export the consolidated data
consolidated_data.to_csv('consolidated_financial_data.csv', index=False)
        

{
  "consolidated_financial_data": {
    "2025-01-01": {
      "sales": 50000,
      "procurement": 30000,
      "operations": 20000,
      "net_cash_position": 0
    },
    "2025-01-02": {
      "sales": 60000,
      "procurement": 35000,
      "operations": 25000,
      "net_cash_position": 0
    },
    ...
  }
}
        

1. Access data from sales, procurement, and operations departments.
2. Clean and validate the incoming data streams.
3. Merge the datasets based on common identifiers such as date.
4. Ensure data consistency and resolve any discrepancies.
5. Provide consolidated reports for real-time financial monitoring.

import pandas as pd

# Load previous day's closing balance
df_previous = pd.read_csv('previous_cash_balance.csv')
opening_balance = df_previous['closing_balance'].iloc[-1]

# Load today's transactions
df_transactions = pd.read_csv('today_transactions.csv')
inflows = df_transactions[df_transactions['type'] == 'inflow']['amount'].sum()
outflows = df_transactions[df_transactions['type'] == 'outflow']['amount'].sum()

# Calculate closing balance
closing_balance = opening_balance + inflows - outflows

# Generate daily cash position report
daily_report = {
    'date': '2025-01-19',
    'opening_balance': opening_balance,
    'total_inflows': inflows,
    'total_outflows': outflows,
    'closing_balance': closing_balance
}

df_report = pd.DataFrame([daily_report])
df_report.to_csv('daily_cash_position.csv', index=False)
        

date,opening_balance,total_inflows,total_outflows,closing_balance
2025-01-19,50000,10000,8000,52000
        

1. Retrieve the previous day's closing balance.
2. Collect today's transaction data categorized as inflows and outflows.
3. Compute the closing balance by adjusting for inflows and outflows.
4. Generate a daily cash position report detailing opening balance, total inflows, total outflows, and closing balance.
5. Distribute the report to relevant stakeholders.

import pandas as pd

# Current financial positions
cash_reserves = 100000
investments = 200000

# Calculate optimal ratio
optimal_ratio = cash_reserves / (cash_reserves + investments)

# Determine allocation
recommended_cash = (cash_reserves + investments) * 0.4
recommended_investments = (cash_reserves + investments) * 0.6

# Generate allocation report
allocation_report = {
    'current_cash': cash_reserves,
    'current_investments': investments,
    'recommended_cash': recommended_cash,
    'recommended_investments': recommended_investments
}

df_allocation = pd.DataFrame([allocation_report])
df_allocation.to_csv('liquidity_allocation.csv', index=False)
        

{
  "liquidity_allocation": {
    "current_cash": 100000,
    "current_investments": 200000,
    "recommended_cash": 120000,
    "recommended_investments": 180000
  }
}
        

1. Assess current levels of liquid cash and investments.
2. Evaluate projected cash flow needs based on forecasts.
3. Determine the optimal cash-to-investment ratio to balance liquidity and growth.
4. Recommend adjustments to asset allocations to achieve the optimal balance.
5. Generate and distribute the liquidity allocation report.

import pandas as pd

# Current credit facilities
credit_lines = {
    'Bank A': 50000,
    'Bank B': 75000
}

# Calculate total available credit
total_credit = sum(credit_lines.values())

# Monitor utilization
current_utilization = 0.4

# Generate credit report
credit_report = {
    'Bank A': {'credit_limit': 50000, 'utilization': credit_lines['Bank A'] * current_utilization},
    'Bank B': {'credit_limit': 75000, 'utilization': credit_lines['Bank B'] * current_utilization},
    'Total Credit': total_credit,
    'Overall Utilization': current_utilization
}

df_credit = pd.DataFrame(credit_report)
df_credit.to_csv('credit_facilities.csv', index=False)
        

Bank A Credit Limit,50000
Bank A Utilization,20000
Bank B Credit Limit,75000
Bank B Utilization,30000
Total Credit,125000
Overall Utilization,0.4
        

1. Evaluate current credit facilities and their limits.
2. Monitor the utilization rates of existing credit lines.
3. Identify potential cash flow gaps based on forecasts.
4. Recommend establishing additional credit lines or short-term loans as needed.
5. Generate and share the credit facility report with the finance team.

import pandas as pd

# Define credit policy
credit_policy = {
    'credit_limit': 15000,
    'payment_terms': 'Net 30',
    'late_fee': '3% per month'
}

# Load customer data
df_customers = pd.read_csv('customer_data.csv')

# Apply credit policy based on customer credit scores
def apply_policy(row):
    if row['credit_score'] >= 750:
        return {'credit_limit': 20000, 'payment_terms': 'Net 45', 'late_fee': '2% per month'}
    elif row['credit_score'] >= 600:
        return credit_policy
    else:
        return {'credit_limit': 5000, 'payment_terms': 'Net 15', 'late_fee': '5% per month'}

df_customers[['credit_limit', 'payment_terms', 'late_fee']] = df_customers.apply(apply_policy, axis=1, result_type='expand')

# Export updated customer credit policies
df_customers.to_csv('updated_credit_policies.csv', index=False)
        

customer_id,credit_score,credit_limit,payment_terms,late_fee
C001,800,20000,Net 45,2% per month
C002,650,15000,Net 30,3% per month
C003,550,5000,Net 15,5% per month
...
        

1. Define the organization's credit policy parameters.
2. Analyze customer credit scores and payment histories.
3. Customize credit terms based on customer risk profiles.
4. Implement the credit policies across the customer database.
5. Monitor and adjust policies as needed to mitigate risks.

import pandas as pd
from datetime import datetime

# Load accounts receivable data
df_ar = pd.read_csv('accounts_receivable.csv')

# Calculate days outstanding
df_ar['days_outstanding'] = (pd.to_datetime('today') - pd.to_datetime(df_ar['invoice_date'])).dt.days

# Categorize aging
def categorize(days):
    if days <= 30:
        return 'Current'
    elif days <= 60:
        return '30-60 Days'
    elif days <= 90:
        return '60-90 Days'
    else:
        return 'Over 90 Days'

df_ar['aging_category'] = df_ar['days_outstanding'].apply(categorize)

# Generate aging report
aging_report = df_ar.groupby('aging_category').agg({'amount': 'sum', 'invoice_id': 'count'}).reset_index()
aging_report.columns = ['Aging Category', 'Total Amount', 'Number of Invoices']

# Export the aging report
aging_report.to_excel('accounts_receivable_aging_report.xlsx', index=False)
        

Aging Category,Total Amount,Number of Invoices
Current,100000,50
30-60 Days,50000,30
60-90 Days,30000,20
Over 90 Days,20000,10
        

1. Extract current accounts receivable data.
2. Calculate the number of days each invoice has been outstanding.
3. Categorize each account based on aging brackets (e.g., 0-30, 31-60 days).
4. Aggregate data to identify total amounts and number of overdue invoices in each category.
5. Generate and distribute the aging report to the collections team.

import pandas as pd
from datetime import timedelta
import smtplib
from email.mime.text import MIMEText

# Load recurring invoices
df_invoices = pd.read_csv('recurring_invoices.csv')

# Generate invoice due dates
df_invoices['due_date'] = pd.to_datetime(df_invoices['invoice_date']) + timedelta(days=30)

# Validate invoice data
def validate_invoice(row):
    if row['amount'] <= 0:
        return False
    return True

df_invoices['is_valid'] = df_invoices.apply(validate_invoice, axis=1)
df_valid_invoices = df_invoices[df_invoices['is_valid']]

# Send invoices via email
def send_invoice(row):
    msg = MIMEText(f"Dear {row['customer_name']},\n\nPlease find your invoice attached.\nAmount Due: ${row['amount']}\nDue Date: {row['due_date'].date()}\n\nThank you.")
    msg['Subject'] = 'Your Monthly Invoice'
    msg['From'] = 'finance@yourcompany.com'
    msg['To'] = row['customer_email']
    
    with smtplib.SMTP('smtp.yourcompany.com') as server:
        server.send_message(msg)

for index, invoice in df_valid_invoices.iterrows():
    send_invoice(invoice)

# Track sent invoices
df_valid_invoices.to_csv('sent_invoices.csv', index=False)
        

invoice_id,customer_name,customer_email,amount,invoice_date,due_date,is_valid
INV1001,ABC Corp,contact@abccorp.com,5000,2025-01-01,2025-01-31,True
INV1002,XYZ Ltd,finance@xyzltd.com,3000,2025-01-05,2025-02-04,True
...
        

1. Identify recurring invoices from the invoice database.
2. Calculate due dates based on predefined payment terms.
3. Validate invoice details to ensure accuracy and completeness.
4. Automate the dispatch of invoices to customers via email.
5. Track and record the status of sent invoices for follow-up.
6. Generate reports on invoicing efficiency and collection rates.