/**
 * Fuzzy match against the sanctions DB.
 *
 * Scoring strategy (0-100):
 *   - Exact ID match (passport, IČO, tax_id) → 100, matched_on='id'
 *   - Token-set Jaccard with edit-distance fallback for misspellings
 *   - Best score across primary_name + aliases
 *   - Optional DOB / nationality filters reduce false positives
 *
 * Designed for batches < 1000 candidates (DB pre-filter does the heavy lifting).
 */
import { SanctionsDb } from './db.js';
import type { MatchResult } from './types.js';
export interface SearchOptions {
    threshold?: number;
    limit?: number;
    typeFilter?: 'person' | 'entity';
}
export declare class SanctionsSearch {
    private readonly db;
    constructor(db: SanctionsDb);
    searchByName(rawName: string, opts?: SearchOptions & {
        dob?: string;
        nationality?: string;
    }): MatchResult[];
    /** Look up a CZ company by IČO. Tries exact-id table then name search via ARES (caller-supplied name). */
    searchByIco(ico: string, fallbackName?: string): MatchResult[];
    /** Look up a person by passport / national ID etc. */
    searchByDocument(type: string, value: string): MatchResult[];
}
/**
 * Token-set ratio (industry standard for fuzzy name matching, e.g. FuzzyWuzzy).
 *
 * Splits both names into token sets, isolates the intersection, then takes
 * max(intersection vs full-a, intersection vs full-b, full-a vs full-b).
 * Effect:
 *  - "Bank" vs "Bank Rossiya" → 100 (subset of tokens matches fully)
 *  - "John Smyth" vs "John Smith" → ~90 (typo tolerated)
 *  - "Smith, John" vs "John Smith" → 100 (token order ignored)
 *  - "Jane Doe" vs "Vladimir Putin" → low (no overlap)
 *
 * Sanctions screening intentionally favors recall (false positives reviewed,
 * false negatives missed = compliance breach).
 */
export declare function nameSimilarity(a: string, b: string): number;
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