Finance
20 columns
200 rows
8 formats

Microloan Application Approval Logs

This dataset provides comprehensive logs of microloan application approvals and rejections, including detailed decision reasons, risk scores, fintech innovation indicators, and processing times. It enables in-depth analysis of lending criteria, risk assessment models, and the impact of fintech innovations on loan decisions, supporting both operational optimization and regulatory compliance.

last updated 21 Jul 2025
by GoMask

The brief that made it

Credit risk modeling and scorecard development

Sample rows

preview · 8 of 200 rows · all 20 columns

application_idstring	application_statusstring	risk_scorefloat	fintech_innovation_flagboolean	approval_reason_codestring	customer_idstring	application_datedatetime	loan_amount_requestedfloat	loan_amount_approvedfloat	loan_term_monthsinteger	interest_ratefloat	approval_datedatetime	rejection_datedatetime	rejection_reason_codestring	reviewer_idstring	review_channelstring	supporting_documents_flagboolean	decision_time_secondsinteger	approval_reason_descriptionstring	rejection_reason_descriptionstring
APP00001	approved	74.5	true	RISK_OK	CUST001	2018-03-12T09:15:34	150	150	2	34.2	2018-03-12T13:14:02	blank	blank	AUTO001	automated	false	14308	Applicant shows acceptable risk profile for microloan.	blank
APP00002	approved	59.2	false	PARTIAL_APPROVAL	CUST002	2019-07-20T16:11:10	2500	1800	15	13.9	2019-07-21T09:22:23	blank	blank	REV125B	manual	true	61373	Approved lower amount due to moderate risk score.	blank
APP00003	rejected	39.9	false	blank	CUST003	2020-01-15T10:08:27	18500	0	48	7.2	blank	2020-01-15T16:20:54	OVER_AMOUNT	REV110C	manual	true	22347	blank	Requested loan exceeds eligibility for applicant profile.
APP00004	approved	60.5	false	INCOME_OK	CUST004	2021-02-02T08:35:48	800	800	12	19.5	2021-02-02T09:42:12	blank	blank	AUTO002	automated	true	4024	Income level supports requested loan.	blank
APP00005	rejected	15.3	false	blank	CUST005	2020-12-19T15:19:51	340	0	3	28.4	blank	2020-12-19T18:47:03	LOW_SCORE	AUTO003	automated	false	12432	blank	Applicant risk score is below acceptable threshold.
APP00006	approved	91.7	true	GOOD_HISTORY	CUST006	2018-09-03T13:05:19	48000	25000	60	2.9	2018-09-04T11:09:41	blank	blank	VIP-REV1	manual	true	80062	Applicant has strong repayment history and stable profile.	blank
APP00007	approved	68.2	true	ALT_DATA_OK	CUST007	2019-11-18T17:20:20	1000	1000	18	13.7	2019-11-19T10:15:45	blank	blank	AI-REV01	hybrid	false	61025	Alternative data confirms applicant's creditworthiness.	blank
APP00008	rejected	52.4	false	blank	CUST008	2021-07-13T12:51:09	75	0	1	44.6	blank	2021-07-13T13:45:08	DOCS_MISSING	AI-REV02	automated	false	3200	blank	Required supporting documents were not provided.
APP00009	approved	61.2	true	PROMO	CUST001	2022-02-24T14:02:11	700	700	12	18.8	2022-02-24T15:27:15	blank	blank	REV200Z	hybrid	false	5104	Promotional offer for repeat customers.	blank
APP00010	rejected	21.5	false	blank	CUST009	2017-10-28T09:41:08	54000	0	60	3.5	blank	2017-10-29T18:20:42	NON_RESIDENT	BOARD001	manual	true	117574	blank	Applicant does not meet residency requirements.
APP00011	approved	70.8	true	EMPLOYMENT_OK	CUST010	2019-04-03T11:30:50	3500	3500	18	10.9	2019-04-03T20:35:21	blank	blank	AI-REV03	hybrid	true	32671	Employment confirmed and stable.	blank
APP00012	cancelled	0	false	blank	CUST011	2022-05-14T08:19:33	0.01	0	2	100	blank	blank	blank	blank	automated	false	0	blank	blank
APP00013	rejected	45.2	false	blank	CUST012	2020-06-07T10:45:16	12000	0	36	24.5	blank	2020-06-08T14:18:29	INCOME_INSUFFICIENT	REV300Y	manual	false	98333	blank	Applicant's declared income insufficient for requested loan.
APP00014	approved	66.4	false	VERIFIED_REF	CUST013	2018-11-10T22:22:08	370	370	3	34.9	2018-11-11T07:45:19	blank	blank	REV210T	manual	true	33991	References were verified and positive.	blank
APP00015	approved	95	true	BOARD_OVERRIDE	CUST002	2021-09-14T11:54:23	21000	18000	60	7.9	2021-09-15T08:03:57	blank	blank	BOARD002	manual	true	72774	Board-level override for high-value applicant.	blank
APP00016	rejected	49.7	false	blank	CUST014	2018-04-21T15:47:39	780	0	6	22.6	blank	2018-04-21T20:01:00	MULTI_APPLICATIONS	AUTO004	automated	false	15201	blank	Multiple applications detected for the same period.
APP00017	rejected	10	false	blank	CUST015	2022-11-18T19:21:12	100000	0	60	2.5	blank	2022-11-19T12:30:44	DEFAULT_HISTORY	BOARD003	manual	true	61572	blank	Default history found in credit report.
APP00018	rejected	7.5	false	blank	CUST016	2021-05-16T14:06:51	275	0	2	41.3	blank	2021-05-16T16:45:30	FRAUD_SUSPECTED	REV101X	hybrid	false	9489	blank	Application flagged for suspected fraud.
APP00019	approved	88.4	true	HIGH_NET_WORTH	CUST017	2018-06-28T08:29:26	4200	3500	24	12.2	2018-06-28T13:10:32	blank	blank	AI-REV04	hybrid	true	17206	Applicant classified as high net worth.	blank
APP00020	rejected	9.6	false	blank	CUST018	2020-03-24T09:13:22	120	0	1	39.8	blank	2020-03-24T11:09:18	LOW_SCORE	AUTO005	automated	false	6956	blank	Risk score too low for approval.

What the 200 rows show

from the 200-row sample

Approved (application status) stands out: mean risk_score is 73.8, against 17.6 for the rest.

40%fintech_innovation_flag = true
59.5median risk_score
3review channels
8rejection reason codes
885.0median loan_amount_requested
160.4median loan_amount_approved

Mean risk_score by application_status200 rows

risk_score200 rows, in bands of 10

Median 59.5, from 0.0 to 100.0.

approval_reason_code107 rows with a value · 93 left blank

RISK_OK19
INCOME_OK15
GOOD_HISTORY12
ALT_DATA_OK12
EMPLOYMENT_OK11
VERIFIED_REF10
BOARD_OVERRIDE7
GOV_BACKED7
HIGH_NET_WORTH6
PROMO5

20 columns by typefrom the column list below

string 9
integer 2
float 4
datetime 3
boolean 2

Columns

20 columns in four groups

blueprint · 20 columns
column	type	description	example
Text 9 columns
`application_id`	string	Unique identifier for each microloan applicationunique	APP00001
`customer_id`	string	Unique identifier for the customer applying for the loan	CUST001
`application_status`	string	Final status of the applicationapproved · rejected · pending · cancelled	approved
`approval_reason_code`	string	Short code representing the main reason for approval (e.g., 'RISK_OK', 'INCOME_OK')11 codes · optional	RISK_OK
`approval_reason_description`	string	Detailed description of the approval reasonoptional	Applicant shows acceptabl…
`rejection_reason_code`	string	Short code representing the main reason for rejection (e.g., 'LOW_SCORE', 'INCOME_INSUFFICIENT')8 codes · optional	OVER_AMOUNT
`rejection_reason_description`	string	Detailed description of the rejection reasonoptional	Requested loan exceeds el…
`reviewer_id`	string	Identifier for the human or automated reviewer who made the approval/rejection decisionoptional	AUTO001
`review_channel`	string	Channel through which the application was reviewed (e.g., 'automated', 'manual', 'hybrid')automated · manual · hybrid	automated
Numbers 6 columns
`loan_amount_requested`	float	Amount of money requested by the applicant0.01 or more	150
`loan_amount_approved`	float	Amount of money approved for the loan (0 if rejected)0 or more	150
`loan_term_months`	integer	Duration of the loan in months1 or more	2
`interest_rate`	float	Interest rate applied to the loan (as percentage, e.g., 12.5)0 to 100	34.2
`risk_score`	float	Numerical risk score assigned to the application (e.g., 0-1 or 0-100)0 to 100	74.5
`decision_time_seconds`	integer	Total time taken (in seconds) from application submission to final decision0 or more	14308
Dates and times 3 columns
`application_date`	datetime	Date and time when the loan application was submitted	2018-03-12T09:15:34
`approval_date`	datetime	Date and time when the application was approved (null if not approved)optional	2018-03-12T13:14:02
`rejection_date`	datetime	Date and time when the application was rejected (null if not rejected)optional	2020-01-15T16:20:54
True or false 2 columns
`fintech_innovation_flag`	boolean	Indicates if the application used a fintech innovation feature (e.g., alternative data, AI scoring)	true
`supporting_documents_flag`	boolean	Indicates whether supporting documents were provided with the application	false

Use it for

A finance dashboard
The fintech_innovation_flag rate, risk_score by application_status and a breakdown of approval_reason_code. Excel, Power BI or Tableau.
Why do the 107 approved rows have a mean risk_score of 73.8?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Applications200
APP0000174.5approved
APP0000259.2approved
APP0000691.7approved
A software demo
Believable applications with customer_id, application_date and loan_amount_requested to fill a screen in front of a buyer.

This dataset200 rows20 columns

Yours1,000 rows20 columns

blueprint · microloan-application-approval-logs

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 20 columns with generation rules behind each one. Open it in Data Factory to retune a column, add your own, wire in foreign keys, and run it at the size you actually need.

Rules it was built with

Each application includes applicant demographics.
Approval status and timestamp provided.
Rejection reason categorized.
Loan purpose and amount included.

Rows1,000 rows10,000 rows100,000 rows1,000,000 rows

Open the blueprint in Data Factory

1 credit per row. Preview 20 rows free. Buy rows for the full run.

Exports: CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence: yours to use, including commercially
API slug: microloan-application-approval-logs

What should your data show?

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Sample rows

What the 200 rows show

Columns

Use it for

A finance dashboard

A root-cause class exercise

A software demo

Make it yours.

Same schema. As many rows as you need.

More in Finance

What should your data show?