Financial Industry Selection: Why Traditional BI Cannot Meet Banks' 'Immediate Risk Control' Query Needs?

Introduction: The "Golden 30 Seconds" of Bank Risk Control

In bank credit approval scenarios, there is a critical time window - the "Golden 30 Seconds."

When an account manager receives a customer applying for a loan, they need to understand in an extremely short time:

• •What is the customer's historical transaction record?
• •Are there any overdue records?
• •What is the current debt ratio?
• •What is the risk level assessment result?

This information determines whether the account manager continues to advance the business and how to design the loan plan. However, traditional BI tools have turned this "Golden 30 Seconds" into a "long wait":

• •Power BI / Tableau: Need to open system, find corresponding report, enter customer number, wait for query results... The entire process takes at least 2-3 minutes.
• •FanRuan / QuickBI: Although preset reports can be used, for ad-hoc queries (like "How many transactions over 100,000 yuan did this customer have in the past 6 months?"), they still cannot respond quickly.

More critically, the financial industry has extremely high requirements for data security, and traditional BI tools often cannot fully meet banks' compliance requirements in private deployment, data masking, and row-level permission control.

This is why more and more banks are exploring AskTable - an AI data query platform specifically designed for the financial industry.

Pain Point 1: The "Preset Report" Dilemma of Traditional BI

Complexity and Variability of Banking Business

Banking business logic is extremely complex, involving hundreds of data tables and thousands of fields. Taking credit business as an example:

• •Customer information table: Basic information, contact details, occupation, income, etc.
• •Transaction流水 table: Time, amount, counterparty, transaction type for each transaction, etc.
• •Credit assessment table: Credit score, risk level, overdue records, etc.
• •Loan records table: Historical loans, repayment status, guarantee information, etc.

The traditional BI tool approach is to design reports in advance:

1. •The data team designs a series of fixed report templates based on business needs, like "Customer Credit Report" and "Loan Approval Dashboard."
2. •Account managers or risk control personnel log into the system, select the corresponding report, enter query conditions (like customer number), and view results.

Problems:

• •Incomplete coverage: Preset reports can only cover common query needs and cannot handle ad-hoc, personalized queries.
• •Update lag: When business rules change (like risk assessment model adjustments), reports need redesign and deployment, with long cycles.
• •Tedious operation: Account managers need to remember the location and usage methods of different reports, with high learning costs.

Case: Account Manager's "Report Maze"

An account manager at a joint-stock bank, Xiao Li, needs to use 10+ different reports daily to support business:

• •"Customer basic information query"
• •"Transaction流水 query"
• •"Credit score query"
• •"Loan history query"
• •"Risk level query"
• •……

For each query, Xiao Li needs to:

1. •Open the BI system
2. •Find the corresponding report in the report list (sometimes flipping through several pages)
3. •Enter the customer number
4. •Wait for query results (usually 10-30 seconds)
5. •If needing to view other dimensions of data, repeat the above process

Xiao Li sighed: "The time I spend on 'finding reports' and 'waiting for queries' every day is more than the time I actually spend analyzing data."

Pain Point 2: Immediate Query Needs vs. BI Tool Response Speed

Immediacy Requirements in Risk Control Scenarios

In bank risk control scenarios, immediacy is a core need:

• •Credit approval: Account managers need to quickly query information in front of customers to decide whether to continue advancing the business.
• •Anti-fraud: When the risk control system discovers abnormal transactions, risk control personnel need to immediately query relevant customer historical behavior to determine if it's fraud.
• •Compliance audit: Auditors need to quickly retrieve transaction records of specific time periods and types for compliance checks.

Problems with traditional BI tools:

1. •Long query path: Multiple clicks, selections, and inputs needed to get results.
2. •Slow response: Complex queries may take 10-30 seconds or even longer.
3. •Cannot follow up: If the first query results aren't detailed enough, need to initiate a new query, with low efficiency.

AskTable: Immediate Queries Through Natural Language

AskTable shortens the query path to the extreme through natural language interaction:

Traditional BI tools:

Open system → Find report → Enter conditions → Wait for results → Re-query
(Total time: 2-3 minutes)

AskTable:

Open AskTable → Ask: "What's Customer A's credit score?" → Get results
(Total time: 10-15 seconds)

More importantly, AskTable supports multi-turn conversation:

• •Round 1: "What's Customer A's credit score?" → Result: 680 points
• •Round 2: "Does he have any overdue records?" → Result: No overdue records
• •Round 3: "What's the total transaction流水 in the past 6 months?" → Result: 1.2 million yuan

This conversational query method completely matches human thinking habits and greatly improves query efficiency.

Case: Risk Control Upgrade of a City Commercial Bank

After a city commercial bank introduced AskTable, risk control personnel's query efficiency improved by 5 times:

• •Query time: Shortened from an average of 2-3 minutes to within 30 seconds.
• •Query frequency: Risk control personnel's daily average queries increased from 20 to 100+ times.
• •Decision quality: Because more dimensional data could be obtained quickly, risk control decision accuracy significantly improved.

Pain Point 3: Data Security and Compliance

Data Security Bottom Line of the Financial Industry

The financial industry has extremely high requirements for data security, mainly reflected in the following aspects:

1. •Data doesn't leave the premises: Customer data must be stored on the bank's own servers and cannot be uploaded to public clouds.
2. •Row-level permission control: Users in different roles can only view data within their permission scope. For example, account managers at branches can only view data for customers of their own branch.
3. •Data masking: Sensitive information (like ID numbers and phone numbers) needs to be masked for display.
4. •Audit logs: All query operations need to be recorded for post-audit.

Compliance Challenges of Traditional BI Tools

Traditional BI tools support private deployment, but still face challenges in practical applications:

• •Complex deployment: Private deployment requires professional IT team support, complex configuration, and long cycles.
• •Tedious permission management: Row-level permission configuration requires a large amount of manual work and is prone to errors.
• •Inflexible data masking: Masking rules are often fixed and cannot be flexibly adjusted for different scenarios.

AskTable: Financial-Level Data Security Solution

AskTable provides a financial-level data security solution:

1. •Private deployment: Supports local deployment or dedicated cloud deployment, ensuring data doesn't leave the premises.
2. •Business semantic layer: Adds a business semantic layer between the database and users, achieving flexible permission control and data masking.
3. •Row-level permissions: Supports flexible multi-dimensional row-level permission control based on role, department, and region.
4. •Audit logs: Records all query operations, including query content, query time, and querying user, for post-audit.

Case: When a large state-owned bank introduced AskTable, they required all data to be stored on the bank's intranet, and account managers at different branches could only view customer data for their own branch. AskTable perfectly met these requirements through private deployment and the business semantic layer.

Pain Point 4: Business Semantic Understanding and SQL Generation Accuracy

Professional Terminology of Financial Business

The financial industry has a large amount of professional terminology and business rules, for example:

• •Non-performing loan ratio: The ratio of loans overdue for more than 90 days to total loans.
• •Provision coverage ratio: The ratio of loan loss provisions to non-performing loan balance.
• •Capital adequacy ratio: The ratio of bank capital to risk-weighted assets.

Traditional BI tools cannot understand these business terms. Users need to write complex calculation formulas or SQL statements themselves.

AskTable: Intelligent Analysis of Business Semantic Layer

AskTable maps business terminology to database fields and calculation logic through the business semantic layer:

• •User asks: "What's our bank's non-performing loan ratio?"
• •AskTable automatically understands the definition of "non-performing loan ratio," generates corresponding SQL statements, and returns results.

Technical implementation:

1. •Business terminology library: Pre-defines common terminology and calculation rules for the financial industry.
2. •Natural language understanding: Uses AI models to parse user questions and identify business terminology.
3. •SQL generation: Automatically generates accurate SQL statements based on business terminology definitions.
4. •Result verification: Performs semantic verification on generated SQL to ensure accuracy.

Case: After a rural commercial bank used AskTable, account managers could directly ask "What's this customer's debt ratio?" AskTable automatically calculated the debt ratio (total liabilities / total assets), without requiring the account manager to understand the underlying calculation logic.

Comparison Summary: Traditional BI vs AskTable

Real Case: Digital Transformation of a Joint-Stock Bank

Background

The bank has over 200 branches nationwide and 3,000+ account managers. Initially used traditional BI tools for data queries but faced the following problems:

1. •Account managers depended on headquarters: All ad-hoc data query needs had to be submitted to the headquarters data team, with cycles of 1-2 days.
2. •Low query efficiency: Account managers averaged 10+ data queries daily, but each query took 2-3 minutes, seriously affecting work efficiency.
3. •Data security risks: Due to inconvenient queries, some account managers exported customer data to local Excel for analysis, posing data leakage risks.

Solutions

The bank introduced AskTable and performed private deployment:

1. •Natural language queries: Account managers could directly ask questions in Chinese, like "What's Customer A's credit score?" and "Does he have any overdue records?"
2. •Row-level permission control: Account managers at different branches could only view data for customers of their own institution.
3. •Data masking: Sensitive information (like ID numbers and phone numbers) was automatically masked for display.
4. •Audit logs: All query operations were recorded for post-audit.

Effects

• •Query efficiency improved 10 times: Account managers' average query time shortened from 2-3 minutes to 10-15 seconds.
• •Query frequency increased 3 times: Because queries became simple and convenient, account managers' daily average queries increased from 10 to 30+ times.
• •Data security risks reduced: Because queries were convenient, account managers no longer needed to export data locally, significantly reducing data leakage risks.
• •Business efficiency improved: Account managers could quickly query information in front of customers, business advancement speed increased, and customer satisfaction improved.

Conclusion: What Kind of Data Tools Does the Financial Industry Need?

Traditional BI tools still have value in headquarters data analysis teams of the financial industry. But for frontline account managers, risk control personnel, and auditors, what they need is:

• •Immediacy: Get key information within the "Golden 30 Seconds."
• •Ease of use: No training needed, speak and use.
• •Security: Meet the financial industry's data security and compliance requirements.
• •Accuracy: Understand financial business terminology and generate accurate query results.

AskTable was created for such needs. Through natural language interaction, business semantic layer, private deployment, and other technologies, AskTable helps financial institutions achieve democratization of data queries - enabling every business personnel to quickly and securely obtain data to support business decisions.

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Learn more: Visit AskTable Official Website or contact us for financial industry solutions.