存款分析

1. 简介

零售银行业在管理存款方面面临巨大挑战,原因在于利率波动、客户行为演变以及技术日新月异。预计到 2025 年,存款增长仍将保持低迷,而存款成本却有所上升,这给银行的净利息收入带来了压力。受流动性需求和储户不愿接受较低利率的影响,存款领域的竞争愈发激烈。此外,宏观经济的逆风和政治不确定性也影响了消费者的行为和企业的借贷需求。银行正专注于获取和留存新客户、个性化定价策略以及卓越的数字体验,以应对这些挑战。

2. 应用场景

图数据库擅长分析零售银行存款的复杂性。通过利用客户、账户和交易之间的关系,图数据库使银行能够建模并可视化客户行为。这有助于银行优化定价策略、识别潜在风险,并根据个人需求和市场趋势实现个性化服务。

与传统系统相比,图数据库提供实时分析、更快的响应时间和卓越的数据建模能力。这些优势转化为零售银行客户体验的提升、风险管理的增强以及盈利能力的增加。

3. 解决方案

图数据库提供了一种分析银行存款的独特方法,超越了传统关系型数据库的局限。图数据库对客户、账户和交易之间复杂的关联和模式进行建模,这对于理解存款行为至关重要。这与《理赔欺诈》文档中所述的图数据库在欺诈检测中的应用非常相似。利用图论,图数据库可以准确地表示实体及其连接,从而通过清晰展现数据点之间的关系,帮助银行更好地管理存款。它们允许对数据集内的连接进行更复杂的分析,而不仅仅是查看数据集本身简单的内容。这种方法促进了实时分析,从而能够对市场或客户活动的变化做出更迅速的反应,而这是传统系统难以做到的。

3.1. 图数据库如何提供帮助?

  • 链接分析 (Link Analysis):正如 Neo4j 可以通过探索连接来揭示复杂的模式一样,它也可以探索客户人口统计、账户活动和交易历史之间的连接,以揭示复杂的存款模式。这有助于银行可视化不同因素如何相互作用并影响存款趋势。

  • 模式检测 (Pattern Detection):类似于图数据库在没有预设起点的情况下识别欺诈模式的能力,它们可以分析存款数据以揭示隐藏的关系并识别新兴趋势。这包括识别具有相似存款行为的客户群体,或识别与异常资金流相关的账户。

  • 实时分析 (Real-time Analysis):图数据库可以实时监控和分析存款活动。这使得银行能够快速识别存款行为的异常变化,分析动态定价策略的影响,并迅速应对市场变化。

图数据库在处理存款数据这种复杂且相互关联的特性时,比传统系统具有明显的优势。通过采用这项技术,银行可以加强对客户行为的理解并改善其存款策略。这种方法与图数据库在欺诈检测中的有效应用相呼应。

4. 建模

本节将展示示例图上的 Cypher 查询示例。目的是说明查询的结构,并提供关于如何在实际环境中构建数据的指南。我们将在一个包含少量节点的图上进行演示。示例图将基于以下数据模型:

4.1. 数据模型

fs deposit analysis date model

4.1.1 必填字段

存款分析所需的最小字段

Customer(客户)节点

  • customerId:客户的唯一标识符

Account(账户)节点

  • accountId:账户的唯一标识符

  • accountNumber:账号

Transaction(交易)节点

  • transactionId:交易的唯一标识符

  • amount:交易金额

  • timestamp:交易时间戳

  • type:交易类型(例如 "CASH_DEPOSIT")

关系

  • HAS_ACCOUNT:连接“客户”节点到“账户”节点

  • PERFORMS:连接“账户”节点到“交易”节点(支出)

  • BENEFITS_TO:连接“交易”节点到“账户”节点(收入)

4.2. 演示数据

// Create customers
CREATE (c1:Customer {customerId: "CUST_001"})
CREATE (c2:Customer {customerId: "CUST_002"})
CREATE (c3:Customer {customerId: "CUST_003"})

// Create accounts
CREATE (a1:Account {accountId: "ACC_001", accountNumber: "1000001"})
CREATE (a2:Account {accountId: "ACC_002", accountNumber: "1000002"})
CREATE (a3:Account {accountId: "ACC_003", accountNumber: "1000003"})

// Create relationships - some accounts are shared between customers
CREATE (c1)-[:HAS_ACCOUNT]->(a1)
CREATE (c1)-[:HAS_ACCOUNT]->(a2)
CREATE (c2)-[:HAS_ACCOUNT]->(a2)
CREATE (c2)-[:HAS_ACCOUNT]->(a3)
CREATE (c3)-[:HAS_ACCOUNT]->(a3)

// Create transactions (cash deposits)
CREATE (t1:Transaction {transactionId: "TXN_001", amount: 3000.0, timestamp: datetime()-duration('P2M'), type: "CASH_DEPOSIT"})
CREATE (t2:Transaction {transactionId: "TXN_002", amount: 5000.0, timestamp: datetime()-duration('P1M'), type: "CASH_DEPOSIT"})
CREATE (t3:Transaction {transactionId: "TXN_003", amount: 1000.0, timestamp: datetime(), type: "CASH_DEPOSIT"})
CREATE (t4:Transaction {transactionId: "TXN_004", amount: 4000.0, timestamp: datetime(), type: "CASH_DEPOSIT"})
CREATE (t5:Transaction {transactionId: "TXN_005", amount: 2000.0, timestamp: datetime(), type: "CASH_DEPOSIT"})

// Create transaction relationships
CREATE (a1)-[:PERFORMS]->(t1)-[:BENEFITS_TO]->(a1)
CREATE (a1)-[:PERFORMS]->(t2)-[:BENEFITS_TO]->(a1)
CREATE (a1)-[:PERFORMS]->(t3)-[:BENEFITS_TO]->(a1)
CREATE (a2)-[:PERFORMS]->(t4)-[:BENEFITS_TO]->(a2)
CREATE (a3)-[:PERFORMS]->(t5)-[:BENEFITS_TO]->(a3)

4.3. Neo4j 方案

如果调用

// Show neo4j scheme
CALL db.schema.visualization()

您将看到以下响应,显示带有 HAS_ACCOUNT、PERFORMS 和 BENEFITS_TO 关系的客户、账户和交易节点

fs deposit analysis schema

5. Cypher 查询

这些查询展示了图数据库如何支持关键的存款管理目标:识别一致的储户以进行留存、分析客户关系以挖掘家庭银行业务机会,以及理解存款流模式以制定定价策略。

5.1. 近期存款活动分析

识别所有近期存款活动,以了解当前的存款流和客户参与模式。这有助于流动性规划,并有助于识别那些对有竞争力的利率报价可能做出反应的活跃客户。

// Find all deposits in the last month with customer and account context
MATCH path = (c:Customer)-[:HAS_ACCOUNT]->(a:Account)-[:PERFORMS]->(t:Transaction)-[:BENEFITS_TO]->(a)
WHERE t.timestamp > datetime()-duration('P1M')
AND t.type = "CASH_DEPOSIT"

RETURN path

此查询可视化了存款流模式,展示了客户如何连接到账户及其最近的交易活动。若要获得分析结果,请使用

// Same query returning tabular data for analysis
MATCH (c:Customer)-[:HAS_ACCOUNT]->(a:Account)-[:PERFORMS]->(t:Transaction)-[:BENEFITS_TO]->(a)
WHERE t.timestamp > datetime()-duration('P1M')
AND t.type = "CASH_DEPOSIT"

RETURN
    c.customerId,
    a.accountNumber,
    t.amount,
    t.timestamp
ORDER BY t.timestamp DESC

商业价值:为流动性管理提供存款活动的实时视图,并识别近期活跃的客户以进行针对性的留存活动。

5.2. 持续高价值储户识别

识别表现出持续存款行为的客户——这些是需要留存和采取溢价定价策略的高价值客户。该查询查找具有持续存款活动和稳定存款水平的客户。

业务目标

这些客户是以下活动的最佳人选:

  • 优惠存款利率和个性化定价

  • 竞争期间的留存活动

  • 关系银行业务及其他产品供应

  • 优先客户服务和专属客户经理管理

// Find consistent high-value depositors for retention and premium pricing
MATCH (c:Customer)-[:HAS_ACCOUNT]->(a:Account)-[:PERFORMS]->(t:Transaction)-[:BENEFITS_TO]->(a)
WHERE t.type = "CASH_DEPOSIT"
AND t.timestamp > datetime()-duration('P3M')

WITH c, a, t,
  CASE
    WHEN t.timestamp > datetime()-duration('P1M') THEN 'current'
    ELSE 'prior_two_months'
  END AS period

WITH c, period, SUM(t.amount) AS total_deposits, COUNT(t) AS transaction_count
WHERE total_deposits > 0

WITH c,
  COALESCE(SUM(CASE WHEN period = 'current' THEN total_deposits END), 0) AS current_month,
  COALESCE(SUM(CASE WHEN period = 'prior_two_months' THEN total_deposits END), 0) AS prior_two_months,
  COALESCE(SUM(CASE WHEN period = 'current' THEN transaction_count END), 0) AS current_txn_count,
  COALESCE(SUM(CASE WHEN period = 'prior_two_months' THEN transaction_count END), 0) AS prior_txn_count

// Filter for consistent depositors (maintained at least 50% of deposit level)
WHERE current_month > 0
AND prior_two_months > 0
AND (current_month / prior_two_months) >= 0.5

// Return customer analysis with retention priority scoring
MATCH (c)-[:HAS_ACCOUNT]->(a:Account)
RETURN
    c.customerId,
    current_month,
    prior_two_months,
    current_month + prior_two_months AS total_deposits_3m,
    current_txn_count + prior_txn_count AS total_transactions,
    (current_month + prior_two_months) / (current_txn_count + prior_txn_count) AS avg_deposit_size,
    current_month / prior_two_months AS consistency_ratio,
    count(DISTINCT a) AS account_count,
    // Priority score for retention campaigns
    (current_month + prior_two_months) * (current_month / prior_two_months) AS retention_priority_score
ORDER BY retention_priority_score DESC

商业价值:识别应获得优惠定价、专属客户经理服务和在竞争期间优先进行留存工作的高价值、持续性储户。

5.3. 家庭与关系银行分析

识别共享账户的客户,以了解家庭关系和联合财务管理。这有助于实现家庭层面的存款策略、家庭银行套餐和基于关系的定价。

业务目标

了解账户共享模式有助于:

  • 家庭银行策略:提供家庭账户套餐和联合定价

  • 关系留存:失去一名家庭成员可能会影响多个账户

  • 交叉销售机会:识别家庭单位以提供全面的金融服务

  • 风险评估:了解家庭单位内的存款集中度

// Visualize household banking relationships and shared account networks
MATCH path = (c1:Customer)-[:HAS_ACCOUNT]->(a:Account)<-[:HAS_ACCOUNT]-(c2:Customer)
WHERE c1.customerId < c2.customerId  // Avoid duplicate pairs

// Include transaction paths for shared accounts
OPTIONAL MATCH txPath = (a)-[:PERFORMS]->(t:Transaction)-[:BENEFITS_TO]->(a)
WHERE t.timestamp > datetime()-duration('P3M')
AND t.type = "CASH_DEPOSIT"

// Return all paths showing household connections
WITH path, collect(txPath) as transactionPaths
LIMIT 25

RETURN path, transactionPaths

此可视化通过显示通过共享账户连接的客户及其交易流,揭示了家庭银行网络。进行家庭层面的分析

// Analyze household/relationship banking patterns through shared accounts
MATCH (c1:Customer)-[:HAS_ACCOUNT]->(a:Account)<-[:HAS_ACCOUNT]-(c2:Customer)
WHERE c1.customerId < c2.customerId  // Avoid duplicate pairs
WITH c1, c2, a

// Get deposit activity for shared accounts over last 3 months
OPTIONAL MATCH (a)-[:PERFORMS]->(t:Transaction)-[:BENEFITS_TO]->(a)
WHERE t.timestamp > datetime()-duration('P3M')
AND t.type = "CASH_DEPOSIT"

// Calculate household deposit metrics
WITH c1, c2, a,
     count(t) as depositCount,
     coalesce(sum(t.amount), 0) as totalDeposits,
     coalesce(avg(t.amount), 0) as avgDepositSize

// Aggregate at household level
WITH c1, c2,
     collect(a.accountNumber) as sharedAccounts,
     count(a) as numberOfSharedAccounts,
     sum(depositCount) as totalTransactions,
     sum(totalDeposits) as householdDepositValue,
     avg(avgDepositSize) as avgHouseholdDepositSize

// Calculate household banking potential
RETURN
    c1.customerId as primaryCustomer,
    c2.customerId as relationshipCustomer,
    numberOfSharedAccounts,
    householdDepositValue,
    totalTransactions,
    avgHouseholdDepositSize,
    // Household value score for relationship banking offers
    householdDepositValue * numberOfSharedAccounts as householdValue
ORDER BY householdValue DESC

商业价值:实现家庭层面的存款策略、关系银行套餐,并有助于识别高价值家庭单位以进行优质服务和留存工作。

6. 图数据科学 (GDS)

图数据科学 (Graph Data Science) 算法通过分析客户行为模式、识别高价值客户群体并了解存款流网络,帮助银行优化存款策略。这些见解支持个性化定价策略、客户留存工作以及在存款市场中的竞争定位。

6.1. 个性化定价的客户细分

社区检测 (Community detection) 有助于识别具有相似存款行为的客户群,使银行能够制定有针对性的定价策略和个性化优惠。这直接支持了实施个性化定价策略以争夺存款的业务目标。

客户-账户二分投影

首先,创建一个捕获客户-账户关系的重点投影

// Create customer-account bipartite graph for segmentation analysis
CALL gds.graph.project(
    'customerAccountNetwork',
    ['Customer', 'Account'],
    'HAS_ACCOUNT'
)

客户细分的社区检测

// Run Louvain algorithm to identify customer segments
CALL gds.louvain.stream('customerAccountNetwork')
YIELD nodeId, communityId
WITH gds.util.asNode(nodeId) AS node, communityId
WHERE 'Customer' IN labels(node)

// Analyze segment characteristics
MATCH (c:Customer)-[:HAS_ACCOUNT]->(a:Account)-[:PERFORMS]->(t:Transaction)
WHERE c.customerId = node.customerId
AND t.type = 'CASH_DEPOSIT'
AND t.timestamp > datetime()-duration('P3M')

WITH communityId, c,
     count(t) as depositFrequency,
     sum(t.amount) as totalDeposits,
     avg(t.amount) as avgDepositSize

RETURN
    communityId,
    count(c) as customersInSegment,
    avg(depositFrequency) as avgDepositFrequency,
    avg(totalDeposits) as avgTotalDeposits,
    avg(avgDepositSize) as avgDepositSize
ORDER BY avgTotalDeposits DESC

商业价值: - 识别具有相似存款模式的客户细分 - 为不同细分市场实现针对性的定价策略 - 根据细分市场行为支持个性化留存优惠 - 帮助优化特定客户群的营销活动

6.2. 高价值客户识别

中心度分析 (Centrality analysis) 识别对银行存款策略最有价值的客户,帮助确定留存工作和竞争响应的优先顺序。

通过共享账户实现客户对客户的网络连接

// Create customer network based on shared accounts and deposit volumes
CALL gds.graph.project.cypher(
    'customerDepositNetwork',
    'MATCH (c:Customer) RETURN id(c) AS id, labels(c) AS labels',
    'MATCH (c1:Customer)-[:HAS_ACCOUNT]->(a:Account)<-[:HAS_ACCOUNT]-(c2:Customer)
     WITH c1, c2, a
     MATCH (a)-[:PERFORMS]->(t:Transaction)
     WHERE t.type = "CASH_DEPOSIT" AND t.timestamp > datetime()-duration("P3M")
     RETURN id(c1) AS source, id(c2) AS target, sum(t.amount) AS weight'
)

PageRank 客户重要性评分

// Identify high-value customers based on network importance
CALL gds.pageRank.stream('customerDepositNetwork', {
    relationshipWeightProperty: 'weight'
})
YIELD nodeId, score

// Get customer details and deposit behaviour
WITH gds.util.asNode(nodeId) AS customer, score
MATCH (customer)-[:HAS_ACCOUNT]->(a:Account)-[:PERFORMS]->(t:Transaction)
WHERE t.type = 'CASH_DEPOSIT'
AND t.timestamp > datetime()-duration('P3M')

WITH customer, score,
     count(DISTINCT a) as accountCount,
     sum(t.amount) as totalDeposits,
     count(t) as transactionCount

RETURN
    customer.customerId,
    score as networkImportance,
    accountCount,
    totalDeposits,
    transactionCount,
    totalDeposits/transactionCount as avgTransactionSize
ORDER BY score DESC
LIMIT 10

商业价值: - 识别具有最高网络价值的客户以进行优先留存 - 支持高价值客户的竞争响应策略 - 实现主动的客户管理和个性化服务 - 帮助优化客户获取和留存的资源分配

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