AI Retail Returns Management System Development

Development of an AI-based returns management system for retail

Returns cost retailers 10-15% of their revenue due to operational processing costs, loss of product value, and fraud. An ML-based returns management system solves three problems: detecting fraudulent returns, optimizing return/exchange decisions, and forecasting return volumes.

The Anatomy of Reverse Fraud

Typical schemes:

• Wardrobing: purchase for one-time use (dress for an event) → return
• Price arbitrage: purchase at a discount → return → repurchase at an even greater discount
• Receipt fraud: returning an item without a receipt with an inflated "purchase price"
• Bricking: return of broken/replaced goods
• Return-to-shelf fraud: An employee fraudulently issues a return and pockets the money

Indicators of a fraudulent return:

fraud_indicators = {
    # Паттерны по клиенту
    'return_rate_customer': 'выше 30% от покупок = подозрительно',
    'returns_without_receipt_pct': '> 50% = красный флаг',
    'return_frequency': 'несколько возвратов за неделю',
    'high_value_returns': 'возвраты > среднего чека × 3',

    # Паттерны по транзакции
    'time_since_purchase': '> 60 дней = подозрительно для большинства категорий',
    'condition_mismatch': 'новый товар возвращают как "не подошёл" при видимых следах использования',
    'serial_number_mismatch': 'серийный номер не совпадает с чеком',

    # Паттерны по каналу
    'cross_channel_return': 'купил онлайн, возвращает в магазин без оригинальной упаковки',
    'holiday_cycle': 'покупка 25 декабря, возврат 2 января'
}

ML model for fraud detection

Feature Engineering:

def extract_return_features(return_event, customer_history, item_data):
    customer_returns = customer_history[customer_history['type'] == 'return']

    return {
        # Клиентский профиль
        'customer_lifetime_return_rate': len(customer_returns) / len(customer_history),
        'customer_return_value_total': customer_returns['amount'].sum(),
        'days_since_first_purchase': (today - customer_history['date'].min()).days,
        'return_to_purchase_ratio_90d': calculate_return_ratio(customer_history, 90),

        # Текущий возврат
        'days_since_purchase': (today - return_event['purchase_date']).days,
        'return_amount_usd': return_event['amount'],
        'return_amount_vs_avg_purchase': return_event['amount'] / customer_history['amount'].mean(),
        'has_receipt': return_event['receipt_present'],
        'original_channel': return_event['purchase_channel'],
        'return_channel': return_event['return_channel'],
        'cross_channel': return_event['purchase_channel'] != return_event['return_channel'],

        # Товарный профиль
        'item_category_return_rate': item_data['category_avg_return_rate'],
        'item_price_tier': item_data['price_tier'],
        'item_is_seasonal': item_data['is_seasonal'],
        'item_sale_item': item_data['was_on_sale']
    }

Model and interpretation:

from lightgbm import LGBMClassifier
import shap

fraud_model = LGBMClassifier(
    scale_pos_weight=50,  # ~ 2% фрода
    n_estimators=300
)
fraud_model.fit(X_train, y_fraud_train)

def evaluate_return_fraud(return_features):
    score = fraud_model.predict_proba([return_features])[0][1]

    if score > 0.7:
        # SHAP объяснение для сотрудника
        explainer = shap.TreeExplainer(fraud_model)
        shap_values = explainer.shap_values(return_features)
        top_reason = get_top_shap_feature(shap_values)

        return {
            'decision': 'manual_review',
            'fraud_score': score,
            'primary_reason': top_reason,
            'recommended_action': 'verify_serial_number_and_condition'
        }

    return {'decision': 'approve', 'fraud_score': score}

Optimizing Return Decisions

Decision Engine:

def return_decision_engine(return_request, fraud_score, business_rules):
    """
    Матрица решений: fraud_score × customer_tier × business_context
    """
    customer_tier = get_customer_tier(return_request['customer_id'])  # gold/silver/standard
    item_category = return_request['item_category']

    # Высокодоходный клиент + низкий риск → автоматическое одобрение
    if fraud_score < 0.2 and customer_tier == 'gold':
        return 'auto_approve'

    # Высокий риск → ручная проверка или отказ
    elif fraud_score > 0.7:
        return 'manual_review_required'

    # Категории с ограничениями
    elif item_category in ['consumables', 'digital', 'underwear']:
        return 'restricted_return_policy'

    # Базовый поток: стандартное одобрение
    else:
        return 'standard_return_process'

Suggestion of alternatives: Instead of a full refund, we offer exchanges, partial refunds, and store credits. For high-LTV customers, we offer an extended return policy as a retention tool.

Forecasting the volume of returns

Why forecast returns:

• Operational planning: how many employees are needed at the return point
• Inventory: which items will be freed up for resale
• Financial: Provision for returns in financial accounting

def forecast_returns_volume(sales_history, return_rates_by_category, promo_calendar):
    """
    Прогноз возвратов = прогноз продаж × ожидаемый return rate
    с учётом сезонности и промо-эффекта
    """
    # Сезонность возвратов: пик в январе (после рождественских покупок)
    seasonal_return_multiplier = {
        1: 1.8,  # январь — пик
        2: 1.2,
        11: 1.3,  # ноябрь/декабрь — рост перед праздниками
        12: 1.5
    }

    sales_forecast = prophet_sales_model.predict(forecast_horizon=30)

    return_forecast = {}
    for category, sales in sales_forecast.items():
        base_rate = return_rates_by_category[category]
        season_factor = seasonal_return_multiplier.get(forecast_month, 1.0)
        return_forecast[category] = sales * base_rate * season_factor

    return return_forecast

Analysis of recurrent causes

NLP categorization of return reasons:

from transformers import pipeline

return_reason_classifier = pipeline(
    'text-classification',
    model='fine_tuned_return_reason_classifier'
)

return_categories = {
    'wrong_size': 'sizing issue',
    'not_as_described': 'product quality/description mismatch',
    'changed_mind': 'buyer remorse',
    'damaged': 'quality defect',
    'wrong_item': 'fulfillment error',
    'price_change': 'found cheaper elsewhere'
}

def analyze_return_reasons(return_comments):
    """
    Свободный текст клиента → структурированная категория
    """
    predictions = return_reason_classifier(return_comments)
    return [{'text': text, 'category': pred['label'], 'confidence': pred['score']}
            for text, pred in zip(return_comments, predictions)]

Feedback from the product/supply chain:

• Many "not_as_described" returns for a specific SKU → correct description/photo
• Too many "wrong_size" for the category → improve size guide
• A lot of "damaged" → packaging or logistics problem

Timeframe: Fraud detection features + LightGBM + decision engine + basic analytics — 4-5 weeks. NLP return reasons, returns volume forecast, customer tier policies, SHAP explanations — 2-3 months.