use case

Customer Complaints Intake Automation

Financial Services/Banking

Customer Support/Operations

Customer Complaints Management, Intake & Triage, Regulatory Response

Business Challenge

Customer complaints arrive through multiple unstructured channels including email, scanned letters, handwritten notes, branch-captured documents, and mailed correspondence. Each complaint must be manually opened, read, interpreted, categorized, and routed to the correct internal function (e.g., fraud, deposits, prepaid, ACH, card operations, disputes).

Manual review is time-consuming, inconsistent, and creates operational risk—particularly when complaints include complex identifiers such as account numbers, dates, SSN references, program names, intake notes, or third-party details. This slows downstream investigations, affects regulatory response times, and increases the risk of misrouted complaints, incomplete documentation, or untimely resolution.

Fisent BizAI Solution

BizAI automates end-to-end intake by reading and structuring information from complaints across any format, including typed letters, scanned PDFs, photographed documents, and handwritten text.

Using a field-extraction schema defined by the customer, BizAI identifies and normalizes key complaint data elements such as:

Customer Identity and Contact Data
  • First name, last name

  • Business name (if applicable)

  • Related party identifiers

  • Phone number

  • Email address

  • Full mailing address (street, city, state, ZIP, country)

Structured Output
  • BizAI converts each complaint into a normalized JSON structure ready for ingestion into workflow systems

    (e.g., Onspring, ServiceNow, internal case management workflows, etc.).

  • Business name (if applicable)

    BizAI then classifies the complaint category and routes it to the appropriate operations team for investigation & ultimately resolution.

Financial Identifiers
  • Account numbers

    (filtered to the Bank when required)

  • Card numbers

  • Related party identifiers

Program Analysis
  • Auto-classification into a dynamically provided list of 150+ program names (e.g., FinTech programs)

Complaint Metadata
  • Date of the letter

  • Complaint reason (summarized)

  • Contact notes

    (secondary identifiers, DOB/SSN references, case no,, related parties)

Value Delivered

Reduced handling time

Cuts manual intake and classification from minutes per complaint to seconds

Higher routing accuracy

Ensures complaints reach the correct business function correctly the first time

Supports handwritten
and low-quality input

Eliminates dependence on clean, structured digital formats

Regulatory compliance

Strengthens documentation quality and completeness for audits and complaint-response timelines with structured data outputs

Operational scale

Allows the bank to process large influxes of complaints without increasing staff

Data standardization

Produces clean, structured complaint records for downstream analytics and reporting

Mission Critical Process Automation

Regulated Industries Require a Robust Approach to Managing Customer Complaints