AI for Education and Lending: Faster Files with Human Decisions

Why do education and lending belong in one guide?

An admissions office and a lending team would not recognise much in each other’s week, yet their operational problem is nearly identical. Someone applies. Documents arrive in a mess of formats. Details have to match across papers issued by different authorities. A queue builds at exactly the time of year when the team is smallest. And at the end there is a decision about a person’s future that somebody has to be able to justify.

BYBO’s industries view groups the two together for that reason, with the same three jobs listed under each: route enquiries to the right person, check application completeness, and track handoffs and progress, measured by application turnaround time. That is a useful frame, because it puts the work where the delay actually is. In most application processes, the decision takes minutes. The file takes weeks.

What can a system do with education enquiries and applications?

Admission season is a volume problem with a service problem inside it. Parents and students ask the same twenty questions across phone, WhatsApp, web forms and walk-ins, while the team is also assembling files. The clerical half of that can move to a system:

• Capture every enquiry in one place, with the programme asked about, the channel and the language used.
• Route it to the right counsellor by programme, campus, language or the stage the student has reached.
• Answer published questions from approved sources: eligibility criteria, fees as published, deadlines, documents required, hostel details.
• Check application completeness against the list for that programme, and name what is missing.
• Read submitted documents for obvious problems: a name that differs from the certificate, a missing page, an unreadable scan, an expired ID.
• Track the cohort: who has applied, submitted, paid, confirmed or gone quiet, with a reminder before each deadline.

What the system should not do is rank applicants, score them, or interpret an eligibility rule that is not written down. Published criteria can be checked mechanically. Anything requiring interpretation, such as an unusual qualification from another board or a hardship case, goes to a person with the file attached.

What can a system do with a loan file?

The same shape appears in lending, with tighter rules around it. A system can accept an application, list the documents the product needs, read what has been uploaded, compare details across the bank statement, the identity document and the application form, and prepare a file the credit team can assess in one sitting. It can flag what is missing, what does not match and what looks unreadable. It can chase the borrower for the gap.

It cannot take over the assessment. The Reserve Bank of India (Digital Lending) Directions, 2025, dated 8 May 2025, require the regulated entity to obtain information on the borrower’s economic profile, including at a minimum age, occupation and income, to assess creditworthiness before extending any loan, and to keep that on record for audit. Where a lending service provider is involved, the Directions state that an outsourcing agreement shall in no manner dilute or absolve the regulated entity of its obligations, and that it remains fully responsible for the acts and omissions of that provider. Grievance redressal stays with the regulated entity too, with nodal officers designated and their contact details displayed.

Read plainly, that settles the design question. Software can prepare, check, chase and present. Accountability does not move with the work.

Which decisions must stay with people?

Admission and credit decisions, and every exception around them: a waiver, a rejection, a request to reconsider, an unusual document, a case that does not fit the policy. These are decisions about a person, and someone has to own them.

The Reserve Bank’s FREE-AI Committee report, released in August 2025, puts it in principles it calls sutras. Under ‘People First’, AI should augment human decision-making but defer to human judgment, with final authority resting with humans who can override the system. Under ‘Accountability’, entities that deploy AI remain fully accountable for the decisions and outcomes, regardless of the level of automation, and accountability cannot be delegated to the model and underlying algorithm. The report is a committee’s recommendations rather than a regulation, but the principles are a sound design brief for anyone outside finance too.

How do you keep the process fair and explainable?

It is tempting to think that fairness is safe as long as a person signs at the end. It is not. The way a file is prepared shapes the decision: what is flagged, what is summarised, what order the queue is in, which cases carry a warning. A system that quietly pushes certain applicants down the list has affected outcomes without ever deciding anything.

• Decide what the system may never use or infer: caste, religion, gender, region, and the crude proxies for them such as surname, address area or school.
• Test on history. Take a set of past files with known outcomes and compare what the system flags with what your team actually found.
• Compare rejection reasons across groups you can legitimately measure, such as first-time applicants, and ask why any pattern exists.
• Record a reason for every refusal in words the applicant could be given, not a score.
• Publish the checklist. If the requirements are written down, an applicant can fix a gap instead of guessing.
• Sample refused files each month with the process owner, and treat every corrected case as a test case.

The FREE-AI report treats this as a design property rather than a report you write afterwards: outcomes should be fair and non-discriminatory, and understandability should be a core design feature, with disclosures and outcomes that the deploying entity can explain. That is a fair test for an admissions system as much as a credit one. If nobody in the room can say why a file was flagged, the flag should not exist.

What rules apply to the data in these files?

Both sectors handle sensitive material about people who are not in a strong position to argue. India’s Digital Personal Data Protection Act, 2023 applies to all of it. Two provisions matter especially here. Where personal data is likely to be used to make a decision affecting the person, or to be shared with another organisation, it must be complete, accurate and consistent. And a child is anyone under eighteen: processing a child’s personal data requires verifiable consent from a parent or lawful guardian, and tracking, behavioural monitoring and targeted advertising directed at children are not permitted.

For schools and colleges there is a narrow carve-out. The Digital Personal Data Protection Rules, 2025, notified in November 2025, list educational institutions among the classes for whom two of those children’s provisions do not apply, where the processing is restricted to tracking and behavioural monitoring for the institution’s educational activities or in the interests of the safety of children enrolled with it. That is a specific permission for a specific purpose, not a general licence.

On the lending side, the Digital Lending Directions require that data collection through a lending app be need-based, with the borrower’s prior and explicit consent and an audit trail, and that the borrower be able to deny consent for specific data, restrict disclosure to third parties and revoke consent already given. Design for that from the start: collect the fields the file needs, say why, and keep the record of what was agreed.

Where should you start, and what should you measure?

Start with completeness, in one programme or one loan product. It is the least risky part of the file, the easiest to specify and usually the largest share of the delay. Enquiry routing is a good second, because it needs the same records and gives the team back its mornings.

Take those numbers before launch, including at the seasonal peak, and compare like for like afterwards. The document handling itself is Business Operations work: reading a file, checking it against your rules and records, and routing what does not fit to a named reviewer with the evidence attached. The decision at the end of it stays exactly where it was, which is the point of building the rest well.