Reducing acquisition cost by increasing application acceptance rate

Each month, numerous applications are rejected during prospect screening, primarily due to customer risk, high debt behavior, and incorrect information.

Each rejection costs the bank in terms of resources that were spent on screening the prospects:

1. Operational Costs: Processing applications, conducting credit checks, and communicating rejections; all incur expenses.
2. Customer Acquisition Costs: Marketing and advertising expenses aimed at attracting applicants who are ultimately rejected.
3. Reputational Impact: High rejection rates can negatively affect a bank’s reputation, potentially deterring future applicants.
4. High rejection rates signal improvement opportunities in the acquisition process. To enhance efficiency, the bank must understand the reasons for rejections and their trends over time.

This continuous monitoring allows the bank to assess the revenue and cost implications of rejecting prospects. It also supports data-driven decisions to adjust risk thresholds, aiming to boost revenue. The result is a targeted action plan to reduce rejections and increase acceptance rates. Higher acceptance rates lower costs and improve customer satisfaction, fostering a positive market perception of the bank.

The proposed solution entails a detailed, step-by-step analysis of the acquisition pipeline and rejection criteria, employing exploratory analysis methods.

Analysis Output:

1. Rejection Reasons and Trends: Examine the reasons for rejection and their trends over time.
2. Rejection Overhead Costs: Track and evaluate the overhead costs of rejections over time.

Action Plan and P&L Impact:

1. Criteria Modifications: Identify which criteria should be adjusted to lower the rejection rate at the prospect level.
2. NCL & Revenue Balance: Analyze the balance between Net Credit Loss (NCL) and revenue if the acceptance criteria are eased.

These approaches will provide insights into rejection patterns and their financial implications, facilitating informed decision-making to optimize the acquisition process.

This solution is designed for business leaders or teams who aim to:

Streamline their acquisition process:
Ensuring a smooth customer journey in the future requires a streamlined acquisition process.

Continuously improve:
Implement a monitoring process that provides oversight with detailed P&L impact insights.

Enhance business planning using data strategy:
Utilize data intelligence to make informed decisions for customer acquisition, reducing future losses.

Streamline acquisitions process: Ensuring a smooth customer journey in the future requires a streamlined acquisition process.

Continuously improve: Implement a monitoring process that provides oversight with detailed P&L impact insights.

Enhance business planning using data strategy: Utilize data intelligence to make informed decisions for customer acquisition, thereby reducing future losses.

Reduce cost of acquisitions: Each rejection increases the cost of acquiring a new customer due to resource misallocation and generates negative sentiment in the market. This solution will help you develop a strategy to reduce costs in a scientific manner.

Find incremental opportunities by refining acquisition criteria: Monitor stringent criteria in the acquisition strategy and quantify the benefits of relaxing them to meet business targets. This solution will help you simulate the impact of criteria relaxation and make data-driven decisions accordingly.

This solution can be constructed using the following data:

1. Customer-level Profit and Loss (PnL) data
2. Bureau data
3. Data flow from the acquisitions funnel
4. Detailed rejection reason data

This solution utilizes an exploratory analysis approach, analyzing the data in a systematic, step-by-step manner:

1. Identifying areas with elevated rejection rates
2. Investigating the causes of these higher rejections
3. Evaluating the revenue and cost implications of these rejections

1. On-prem ( on customer systems or on DeepQ-AI Environment)
2. On hosted cloud space ( Customer or DeepQ-AI Environment)
   ( deployment is subject to data availability in the same environment, or feasibility of seamless data transfers within secured environments)

1. The final outputs may consist of a report with detailed analysis findings and recommendations.
2. PowerPoint slides or Excel spreadsheets.
3. Interactive dashboards