Finance
20 columns
150 rows
8 formats

Loan Application Approval Trends

This dataset provides detailed records of loan applications, capturing applicant demographics, loan characteristics, processing timelines, and final outcomes. It enables in-depth analysis of approval trends, risk factors, and process bottlenecks to support data-driven lending strategies and operational improvements.

last updated 12 Jul 2025
by GoMask

The brief that made it

Identifying approval and rejection patterns to optimize lending criteria

Sample rows

preview · 8 of 150 rows · all 20 columns

application_idstring	loan_typestring	loan_term_monthsinteger	applicant_countrystring	applicant_idstring	application_datedate	loan_amountfloat	interest_ratefloat	application_statusstring	decision_datedate	approval_reasonstring	processing_time_daysinteger	applicant_incomefloat	applicant_employment_statusstring	applicant_credit_scoreinteger	branch_idstring	officer_idstring	applicant_citystring	applicant_statestring	applicant_postal_codestring
APP10001	auto	60	US	CUST001	2023-12-22	32000	5.7	approved	2023-12-29	Strong credit history	7	58000	employed	712	BR001	OFF01	Chicago	IL	60616
APP10002	business	84	IN	CUST002	2024-03-14	245000	3.9	approved	2024-03-22	Business plan and cash flow positive	8	230000	self-employed	805	BR002	OFF02	Mumbai	MH	400001
APP10003	personal	36	US	CUST003	2024-01-10	18750	8.4	pending	blank	blank	blank	49000	employed	693	BR003	OFF03	Denver	CO	80202
APP10004	student	36	US	CUST004	2024-03-04	1200	7.6	pending	blank	blank	blank	6000	student	621	BR004	OFF04	Austin	TX	73301
APP10005	mortgage	360	CA	CUST005	2024-02-11	750000	2.2	approved	2024-02-21	Prime borrower, high income	10	285000	other	826	BR005	OFF05	Toronto	ON	M5H 2N2
APP10006	personal	48	US	CUST006	2023-11-26	14100	11.3	approved	2023-11-30	Good debt-to-income ratio	4	69000	employed	728	BR006	OFF06	Boston	MA	02108
APP10007	other	2	US	CUST007	2024-03-02	395	25.8	rejected	2024-03-05	Low income and missing documents	3	2100	unemployed	375	BR003	OFF03	Toledo	OH	43601
APP10008	auto	72	US	CUST008	2023-10-15	19500	7.2	approved	2023-10-22	Excellent credit, low risk	7	77000	employed	745	BR007	OFF07	Phoenix	AZ	85001
APP10009	business	60	GB	CUST009	2024-01-28	120000	6.5	pending	blank	blank	blank	156000	self-employed	686	BR002	OFF02	London	ENG	SW1A 1AA
APP10010	personal	60	US	CUST001	2024-02-09	21000	6.8	approved	2024-02-16	High income, stable employment	7	85000	employed	747	BR008	OFF08	San Jose	CA	95112
APP10011	student	48	US	CUST010	2023-09-18	1400	8.9	approved	2023-09-22	Eligible college enrollment	4	9500	student	663	BR009	OFF09	Madison	WI	53703
APP10012	mortgage	480	MC	CUST011	2024-04-01	9500000	1.6	approved	2024-04-15	Ultra-high net worth client	14	2000000	retired	850	BR010	OFF10	Monaco	MC	98000
APP10013	other	1	US	CUST012	2024-03-24	925	99.9	rejected	2024-03-27	Credit score below approval threshold	3	0	unemployed	312	BR011	OFF11	Detroit	MI	48201
APP10014	auto	72	US	CUST013	2023-12-05	38000	6.1	approved	2023-12-11	New vehicle purchase, stable job	6	63000	employed	701	BR012	OFF12	Dallas	TX	75201
APP10015	personal	36	US	CUST014	2024-02-16	21500	8	pending	blank	blank	blank	56000	employed	688	BR013	OFF13	Columbus	OH	43085
APP10016	other	2	US	CUST015	2023-07-02	880	99.9	withdrawn	2023-07-05	Application withdrawn by applicant	3	4200	other	570	BR014	OFF14	Buffalo	NY	14201
APP10017	business	48	ZA	CUST016	2024-04-04	97000	5.2	pending	blank	blank	blank	139000	self-employed	666	BR015	OFF15	Cape Town	WC	8000
APP10018	personal	36	US	CUST017	2023-10-30	14500	9.5	rejected	2023-11-02	Low credit score	3	32000	other	397	BR016	OFF16	Portland	OR	97201
APP10019	mortgage	420	AE	CUST018	2024-01-16	6500000	1.8	approved	2024-01-30	High value collateral provided	14	1200000	employed	834	BR017	OFF17	Dubai	DU	00000
APP10020	student	12	US	CUST019	2023-11-22	500	12.2	pending	blank	blank	blank	4000	student	602	BR018	OFF18	Cleveland	OH	44101

What the 150 rows show

from the 150-row sample

Mortgage (loan type) stands out: mean loan_term_months is 403.3, against 44.2 for the rest.

48median loan_term_months
4application statuses
6applicant employment statuses
17,250median loan_amount
8.2median interest_rate
5median processing_time_days

Mean loan_term_months by loan_type150 rows

loan_term_months150 rows, in bands of 50

Median 48, from 1 to 480.

applicant_country150 rows · 7 values

US116
ZA8
IN7
GB7
CA5
AE4
MC3

20 columns by typefrom the column list below

string 12
integer 3
float 3
date 2

Columns

20 columns in three groups

blueprint · 20 columns
column	type	description	example
Text 12 columns
`application_id`	string	Unique identifier for each loan applicationunique	APP10001
`applicant_id`	string	Unique identifier for each applicant	CUST001
`loan_type`	string	Type of loan applied for (e.g., personal, mortgage, auto, business)6 values	auto
`application_status`	string	Final status of the loan applicationapproved · rejected · pending · withdrawn	approved
`approval_reason`	string	Reason for approval or rejection (e.g., credit score, income, incomplete documents)optional	Strong credit history
`applicant_employment_status`	string	Employment status of the applicant6 values	employed
`branch_id`	string	Identifier for the branch or office where the application was processedoptional	BR001
`officer_id`	string	Identifier for the loan officer who handled the applicationoptional	OFF01
`applicant_city`	string	City where the applicant residesoptional	Chicago
`applicant_state`	string	State or province where the applicant residesoptional	IL
`applicant_postal_code`	string	Postal code of the applicant's residenceoptional	60616
`applicant_country`	string	Country of the applicant's residence7 countries · optional	US
Numbers 6 columns
`loan_amount`	float	Total amount of loan requested by the applicant0 or more	32000
`loan_term_months`	integer	Duration of the loan in months1 or more	60
`interest_rate`	float	Annual interest rate offered for the loan (percentage)0 or more	5.7
`processing_time_days`	integer	Number of days taken to process the application from submission to decision0 or more · optional	7
`applicant_income`	float	Annual income of the applicant at the time of application0 or more	58000
`applicant_credit_score`	integer	Credit score of the applicant at the time of application300 to 850 · optional	712
Dates and times 2 columns
`application_date`	date	Date when the loan application was submitted	2023-12-22
`decision_date`	date	Date when the final decision was made on the applicationoptional	2023-12-29

Use it for

A finance dashboard
Loan_term_months by loan_type and a breakdown of applicant_country. Excel, Power BI or Tableau.
Why do the 18 mortgage rows have a mean loan_term_months of 403.3?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Applications150
APP1000160auto
APP1000284business
APP1000336personal
A software demo
Believable applications with applicant_id, application_date and loan_amount to fill a screen in front of a buyer.

This dataset150 rows20 columns

Yours1,000 rows20 columnsbranch_id: UK only

blueprint · loan-application-approval-trends

Behind this dataset

Same schema. As many rows as you need.

These 150 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

Include only fully completed applications
Flag applications above specified risk threshold
Applications must be timestamped
Approval rate calculated monthly
Rejected applications require reason codes

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: loan-application-approval-trends

What should your data show?

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Sample rows

What the 150 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?