Top 5 Jobs in Financial Services That Are Most at Risk from AI in Lebanon - And How to Adapt

Lebanon's battered banks and nimble fintech startups now sit at the pivot point of an AI-driven shakeup: years of currency collapse, informal capital controls and rising dollarization have pushed customers toward e-wallets, crypto and digital services, creating fertile ground for automation and AI-powered workflows, as covered in a deep look at analysis of Lebanon's resilient fintech sector; at the same time the government has announced a $30–$50M plan for generative AI and digital public infrastructure - including a national digital ID and a Super App - that could speed customer onboarding and payments reform (details of Lebanon's generative AI and digital public infrastructure investment).

Regionally, banks are racing to embed AI into underwriting, fraud detection and service automation, which means Lebanese finance workers need rapid reskilling: practical programs like the AI Essentials for Work bootcamp (Nucamp) teach nontechnical employees how to use AI tools and write better prompts so humans move up the value chain instead of being replaced.

“ChatGPT is the tip of a gigantic AI iceberg.”

Selection relied on Lebanon-focused evidence and practical signals: the ranking leaned heavily on technological‑exposure frameworks described in the SSRN study that applies the Frey–Osborne “task” model to Lebanese data and sector case studies (SSRN study: AI and automation in Lebanon (Frey–Osborne task model)), while industry briefings about how AI reshapes finance helped identify which routine tasks are already being automated - reconciliation, invoice processing and scripted customer support - and which skills buy resilience (LSBF briefing: future finance jobs and how to adapt to AI).

Practical local signals from Nucamp resources on KYC onboarding automation and data‑pipeline design guided the “can‑this‑be‑turned‑into‑a workflow tomorrow?” filter (Nucamp Back End, SQL, and DevOps with Python syllabus (KYC onboarding automation & data‑pipeline design)).

Jobs were scored by task automability, exposure in Lebanese labor statistics, and the feasibility of rapid reskilling (AI literacy, data fluency and judgement), a process that boils down to counting how many routine keystrokes a role contains before it can be fully digitized - a quick, concrete test of “so what?” for workers and employers alike.

Bank tellers in Lebanon face the same squeeze felt worldwide: routine, rule‑bound tasks - cash counts, deposits, basic data entry - are prime targets for automation, and past waves of ATM adoption show that machines first displace tasks before reshaping jobs into higher‑value roles (ATM-to-agent automation arc in banking).

Local banks balancing rising costs and digital demand can deploy teller cash recyclers and branch automation to cut behind‑the‑scenes work dramatically (cash recycler rollout can reduce vault transactions by as much as 80%), freeing staff to focus on sales, complex problem‑solving and onboarding high‑risk customers (cash-handling automation to improve teller performance).

In practical Lebanese terms, the safest pivot is rapid reskilling - move from transactional routines into roles that pair relationship skills with digital fluency: universal banker/advice roles, KYC and digital onboarding specialists, and branch staff who can orchestrate automated workflows; targeted training in KYC onboarding automation and basic data‑pipeline design training turns those seconds saved per transaction into minutes spent growing client trust - a concrete “so what?” that keeps humans at the centre of banking as agentic AI and smarter self‑service take over routine work.

‘Do It For Me' Economy

Loan officers and credit underwriters in Lebanon face both a threat and an opening: AI can automate routine document checks, credit‑score lookups and even parts of decision logic - turning days‑long spreads into minutes‑to‑hours pipelines - yet it also concentrates judgment inside opaque models that can amplify bias and regulatory exposure.

The practical roadmap is straightforward and urgent: combine data‑literacy and model‑oversight skills (SHAP and monotonicity checks from the IE Insights playbook) with hands‑on experience in intelligent document processing and end‑to‑end pipelines so underwriters can validate inputs and spot edge cases; use AI to extend credit to thin‑file SMEs as the MISQ study shows, but couple expansion with fairness testing and human‑in‑the‑loop approval gates; and strengthen relationship and portfolio‑management abilities so officers move from “score clerks” to risk coaches who interpret model outputs for complex, high‑touch borrowers.

Training should prioritise explainability, audit trails and operational controls (IDP + monitoring, which yields the biggest productivity gains reported by industry implementers), so every automated recommendation arrives with a readable rationale and an escalation path - a vivid outcome: fewer piles of paper, more dashboards that flag the one small anomaly that actually matters for a local entrepreneur's loan.

For Lebanon this means reskilling that blends credit judgement, oversight tooling and pipeline design to keep human judgement at the centre of smarter, fairer lending (IE Insights on interpretable credit models, MISQ evidence on AI and inclusion, Back End, SQL, and DevOps with Python bootcamp syllabus (Nucamp)).

“Machine learning models, including XGBoost, can achieve high accuracy in credit scoring but are often "black boxes," making it hard to explain individual decisions.”

Entry-level financial and market/research analysts in Lebanon should read the room: much of the day‑to‑day job - PDF scraping, spreadsheet reformatting and KPI refreshes - looks exactly like the tasks AI already automates, with some estimates warning that up to two‑thirds of entry‑level roles are at risk (Datarails study: How AI Will Affect Entry-Level Finance Jobs).

Newer LLM tools can also beat humans on raw numeric prediction (GPT‑4 scored ~60% vs human analysts at ~53% in one study), and they shine at turning unstructured filings into ready‑to‑analyse tables, chopping data‑prep from days to under an hour - a disruptive productivity win that changes how juniors learn the craft (V7 Labs analysis: Will AI Replace Financial Analysts?).

The practical takeaway for Lebanese juniors is clear: move from being the data‑sweeper to the interpreter - learn AI literacy, basic ETL and model‑validation skills, own narrative and stakeholder communication, and design repeatable pipelines so that human judgement focuses on exceptions and strategy; that single trick - spotting the one contradictory datapoint the AI missed - keeps the analyst indispensable and makes a hiring manager notice in a tight market.

“It's like being in a fraternity and being a pledge.”

For bookkeepers, routine accountants and reconciliation teams in Lebanon, RPA is less a distant threat than a practical tool to climb the value chain: bots can automate invoice matching, intercompany reconciliations and compliance checks so teams stop chasing rows and start owning exceptions, controls and strategic cash‑flow insights; practical guides show RPA's quick ROI on accounts payable/receivable and financial close, and integrating bots with OCR and simple ETL means fewer manual keystrokes and more time for variance analysis and vendor relationship management (CIGNEX: The Impact of RPA in Accounting, SolveXia: RPA in Finance & Accounting).

Success hinges on governance - COEs, change management and clear exception playbooks - so Lebanese firms treat automation as a “gift of time” to redeploy talent into oversight, analytics and advisory roles; imagine a bot running overnight and flagging the single anomalous payment before the morning team starts - an operational detail that turns automation from cost-cutting into client-facing value.

“Given the fact that bots can ‘work' 24 hours a day and are largely a fixed cost, auditors are able to leverage bots to increase audit coverage without adding cost to their organization.”

In Lebanon's banks and insurers, chatbots and voicebots are already reshaping front‑line service: they handle routine balance checks, policy queries and early‑stage debt reminders around the clock, cutting cost and wait times while freeing humans to handle the knotty, high‑stakes cases that really need judgement.

But automation is a double‑edged sword - the CFPB's review of chatbot use in consumer finance warns that poor designs produce wasted time, frustration, inaccurate answers and even legal risk when customers can't reach a human; distressed clients facing fraud or disputed charges may become anxious if automated paths loop or dead‑end without escalation.

Smart deployments used in other markets show a pragmatic middle way: AI voicebots that triage, authenticate and log calls can resolve low‑touch work and auto‑escalate only true exceptions, improving compliance and letting skilled agents focus on the one disagreement or fraud alert that actually matters.

Lebanese firms that pair 24/7 virtual assistants with clear human‑offramps, multilingual support and audit‑ready call logs will avoid the pitfalls the CFPB highlights while turning bots into a force‑multiplier for higher‑value, relationship‑driven service - a practical shift from scripted responses to calm, decisive escalation handling (and a faster, less stressful outcome for the customer).

“So fraud, for example, there's an urgency involved in it, as opposed to somebody who's just calling in to ask a question about mortgage rates in the future…So how does an agent prioritize this [against] the 10 calls that they have? Which ones should they be answering immediately? Which one is on fire? That's the way to think about it.”

Conclusion: the near-term picture for Lebanon's financial services is not a binary “jobs lost” headline but a concrete, local roadmap: firms must pair careful AI governance and explainability with focused automation (KYC, document processing, reconciliation) so routine keystrokes disappear while human judgement - client escalation, complex underwriting, fraud triage - becomes the scarce skill; workers should prioritize AI literacy, basic ETL/data-pipeline skills, and oversight know-how so they can validate models and run exception playbooks rather than chase spreadsheets.

The evidence from Lebanon-focused research and sector forecasts shows this is practical and urgent (SAPUB research: AI in Lebanon's financial sector; 2025 banking and payments sector forecasts).

For employers, the quick wins are modular pilots (IDP + RPA + human-in-the-loop gates) plus clear escalation channels; for employees, short, applied programs bridge the gap - for example the AI Essentials for Work bootcamp - Nucamp (AI skills for work) teaches nontechnical staff how to use AI tools and write effective prompts so saved transaction time converts into higher-value customer work.

The single vivid test: a bot that flags the one anomalous payment before the morning shift turns automation from cost cutting into an opportunity to redeploy talent toward judgement, relationship-building and oversight.