FIN367 – Lecture 6 Comprehensive Study Notes

Sustainable Finance & Bank Lending

  • Context & Motivation
    • Emergence of new sustainability risk – labelled ESG (Environmental, Social, Governance) – is altering bank credit risk landscapes.
    • 6.8 trillion6.8\text{ trillion} global annual lending vs. only 120 billion120\text{ billion} sustainability-linked loans (2020): vast growth headroom.
    • S&P Global Ratings projects sustainability-linked debt issuance rising from 130 billion130\text{ billion} (2020) to >200\text{ billion} (2021).
  • Definition of Sustainable Finance
    • “Financial decisions that take into account ESG factors of an economic activity or project.”
    • Environmental: climate-crisis mitigation, renewable resource use.
    • Social: human/animal rights, consumer protection, diversity in hiring.
    • Governance: management quality, employee relations, compensation practices.
  • Implications for Bank Credit Analysis & Decision
    • Credit officers must consider ESG risk alongside traditional credit metrics.
    • Leads to NEW finance knowledge/practice requirements for graduates.
  • Sustainable Debt Instruments
    • Green Loans (GL): proceeds exclusively finance/refinance eligible green projects.
    • Sustainability-Linked Loans (SLL): proceeds for general purposes, but pricing linked to borrower’s performance against sustainability performance targets (SPTs).
    • Classification governed by Green Loan Principles (GLP) & Sustainability-Linked Loan Principles (SLLP).
  • Principle Frameworks
    • GLP – four core components:
    1. Use of Proceeds
    2. Process for Project Evaluation & Selection
    3. Management of Proceeds
    4. Reporting (transparency & integrity stressed).
    • SLLP – five core components:
    1. KPI Selection
    2. SPT Calibration
    3. Loan Characteristics
    4. Reporting
    5. Verification (clear rationale + alignment to strategy obligatory).
  • Bank-Side Benefits & Strategy Shifts (Accenture study)
    • ESG loans may reach 30 % of total portfolios.
    • Up to 4.5 % higher shareholder returns on high-performing ESG initiatives.
    • Tangible gains: lower funding cost, new revenue streams (non-financial ESG services), brand differentiation.
    • Action plan:
      • Transform lending value chain to net-zero operating model.
      • Reskill staff on ESG criteria.
      • Build ESG data platforms & scoring models.
  • Value-Chain Adjustments (Figure 2 highlights)
    • Credit policy level: integrate ESG appetite, pricing, target share, exclusion lists.
    • Origination: ESG checks, green collaterals (e.g., green mortgage), ESG committees.
    • Monitoring: ESG-adjusted stress-testing, covenant enforcement, termination for non-compliance.
    • Collection: responsible collection & circular asset management.
  • Spectrum of Sustainable Lending Products
    • Green mortgages (rate discounts for energy-efficient homes; e.g., Handelsbanken 0.1 pp discount).
    • Green loan for London office redevelopment (OCBC £85.5 m).
    • Sustainability-linked revolving credit (Xylem & Sustainalytics).
    • Sustainability-linked supply-chain finance (Tesco + Santander).
    • Green loan securitisation (Toyota ABS for EV/hybrid purchases).
  • Industry Case – CIMB Sustainable Finance Framework (2023)
    • Commitments:
      • Net-zero Scope 1 & 2 GHG by 20302030; overall net-zero by 20502050.
      • Exit coal by 20402040; no deforestation/peat/exploitation.
      • Mobilise 60 billion60\text{ billion} towards sustainable finance by 20242024.
      • RM150 million150\text{ million} social impact fund + 100,000100,000 volunteer hours p.a.
    • Alignment with UNEP-FI Principles for Responsible Banking (6 principles: Alignment, Impact & Targets, Clients, Stakeholders, Governance & Culture, Transparency).
  • Internal Tools
    • GSSIPS (Green, Social, Sustainable Impact Products & Services) internal taxonomy.
    • Exclusion list for prohibited activities; mandatory sustainability-risk assessment.

Behavioural Finance & Bank Lending

  • Essence of Behavioural Finance
    • Studies psychological influences on financial behaviour; assumes individuals are bounded-rational, self-control-limited, bias-prone.
    • Critical because credit officers’ irrationalities can heighten bank credit risk.
  • Bias Taxonomy
    • Cognitive Errors (information-processing & memory distortions).
    • Emotional Biases (pain-avoidance / pleasure-seeking impulses).
    • Social Biases (influence of others & stereotypes).
  • Key Cognitive Biases
    • Conservatism (under-reaction to new info).
    • Confirmation (over-weight congruent data; ignore disconfirming).
    • Representativeness (classify new info by similarity to retained category).
    • Illusion of Control.
    • Hindsight (“I knew it all along”).
    • Anchoring & Adjustment.
    • Framing (response changes with wording).
    • Availability (salient data over-used).
  • Prominent Emotional Biases
    • Loss Aversion (loss pain ≈ 2×2\times gain pleasure).
    • Overconfidence / Illusion of Knowledge.
    • Self-Control deficiency.
    • Status Quo preference.
    • Regret Aversion.
  • Consequences for Lending
    • Misjudged borrower risk, mis-priced loans, portfolio concentration.
  • Mitigation Strategies
    • Education & debiasing training.
    • Actively seek disconfirming evidence.
    • Maintain decision logbooks; update probabilities objectively.

Technologies (FinTech) in Bank Lending

  • Credit Scoring Evolution
    • Traditional Scorecards ⇒ Machine Learning (ML) ⇒ Artificial Intelligence (AI).
    • All output PD (probability of default) & LGD (loss-given-default) but data & modelling complexity escalate.
  • Traditional Systems
    • Data: “5 Cs” hard information (Character, Capacity, Capital, Collateral, Conditions).
    • Example: CTOS Malaysia weighting – payment history 45 %, amount owed 30 %, credit history length 15 %, credit mix 10 %, new credit 10 %.
  • Machine Learning Credit Scoring
    • Utilises BIG data (economic, industry, borrower, social, soft info).
    • Workflow:
    1. Historical dataset → feature selection.
    2. Train diverse algorithms (supervised).
    3. Select top performer → accepts “good”, rejects “bad”.
    • Unsupervised ML supports clustering/anomaly detection.
    • Benefits: higher predictive accuracy, dynamic updates.
  • Artificial Intelligence Credit Decisioning
    • AI = programs that sense, reason, act, adapt.
    • ML ⊂ AI; Deep Learning (DL) ⊂ ML (multilayer neural nets).
    • Can handle BIG-COMPLEX multi-modal data (digital footprint, social activity).
    • Tasks: automated underwriting, fraud detection, early-warning, portfolio optimisation.
    • Caution: Hidden algorithmic bias -> regulators (e.g., US Fed) warn of disparate impacts.
  • Quantified Impact on Risk Management
    • Enhances credit scoring, portfolio monitoring, anomaly detection, debt-collection optimisation.
    • Integrates previously unusable big-data sources.
  • Industry Illustrations
    • GFI Fintech (Malaysia): psychometric credit risk assessment.
    • AI-driven platforms provide 24/7 advisory, online loan processing, fraud detection, data protection.

Non-Bank Lending Organisations

Leasing Companies

  • Legal Definition (FSA 2013 §3): letting/sub-letting movable property on hire for business use (ex-hire-purchase).
  • Regulation: Prescribed business; BNM supervisory power (§211, §212).
  • Funding: shareholder equity + bank borrowings.
  • Examples:
    • ORIX Leasing Malaysia (leasing & HP, tech equipment rental, Islamic financing, Tesla special programme).
    • SMFL Leasing (Malaysia) – finance & operating leases, sale-leasebacks, factoring.

Factoring Companies

  • Mechanics: Purchase invoices at 70%−90%70\%-90\% face value; collect at maturity; may manage collections.
  • Legal Status: also prescribed under FSA 2013.
  • Examples: ORIX factoring/LC & Islamic i-Factoring (Bay al-Dayn); Ikhtiar, CapBay, MMAAX, Sunway Credit, etc.

Venture Capital Companies

  • Concept: Provide capital + assume full business risk in exchange for equity/profit share; suits start-ups & tech firms.
  • Exit Options: promoter buyout or IPO.
  • Malaysia Examples:
    • Malaysia Debt Ventures (MDV) – venture & project financing (advanced tech, ICT, green tech, biotech).
    • MAVCAP (RM>1 b fund; E&E focus).
    • Cradle (700+ start-ups funded, equity via DEQ800).
    • MyCreative Ventures, Modal Perdana, etc.

Peer-to-Peer (P2P) Lending

  • Operating Model: Online platforms match borrowers with retail/angel/sophisticated investors; platform handles credit rating, servicing, collections.
  • Malaysia Framework (since May 2016):
    • RM3.87 billion3.87\text{ billion} raised via 54,79154,791 campaigns by Dec-2022.
    • 49%49\% of investors <3535 yrs; 89%89\% funds from retail investors.
    • 70%70\% of issuers raise ≤50,000\leq 50,000, mostly working capital.
  • Operator Requirements: locally incorporated, paid-up capital ≥ RM5 million5\text{ million}; risk scoring, disclosure verification, default management, interest ≤ 18%18\% p.a. (higher requires SC consent).
  • Investor Limits:
    • Retail: ≤ RM50,00050,000 per platform.
    • Angel & Sophisticated: no cap.
  • Eligible Issuers: Malaysia-registered sole prop, partnership, LLP, private/unlisted public company. Must hit ≥80%80\% of target to close campaign. May raise concurrently on multiple platforms with disclosure.
  • Registered P2P Platforms: Funding Societies, CapBay, CapSphere, CrowdSense, QuicKash, MoneySave, Fundaztic, microLEAP, Nusa Kapital, FinPal, Alixco, et al.

Traditional vs. Complementary Credit-Decision Approaches (Lecture Recall)

  • Judgemental (Standard): 5 Cs, CAMPARI, manual officer judgement susceptible to behavioural biases & ESG neglect.
  • Supplementary & Alternative: Credit scoring, ML, AI; incorporation of new risk criteria incl. ESG.

Ethical & Practical Implications

  • ESG: banks have moral duty to finance sustainable transformation; failure carries reputational & regulatory risks.
  • Behavioural Bias Control: promotes fair lending & accurate risk pricing (ethical + prudential).
  • AI Bias & Explainability: need for transparent models to avoid discrimination, ensure accountability.

Connections to Earlier/Foundational Principles

  • 5 Cs of Credit remain baseline but now augmented by ESG metrics & big-data analytics.
  • Risk-Adjusted Return on Capital (RAROC) incorporates ESG & behavioural corrections, aligning with Basel & sustainability frameworks.